+
+## Acknowledge
+
+1. We borrowed a lot of code from [FunASR](https://github.com/modelscope/FunASR).
+2. We borrowed a lot of code from [FunCodec](https://github.com/modelscope/FunCodec).
+3. We borrowed a lot of code from [Matcha-TTS](https://github.com/shivammehta25/Matcha-TTS).
+4. We borrowed a lot of code from [AcademiCodec](https://github.com/yangdongchao/AcademiCodec).
+5. We borrowed a lot of code from [WeNet](https://github.com/wenet-e2e/wenet).
+
+## Disclaimer
+The content provided above is for academic purposes only and is intended to demonstrate technical capabilities. Some examples are sourced from the internet. If any content infringes on your rights, please contact us to request its removal.
\ No newline at end of file
diff --git a/asset/dingding.png b/asset/dingding.png
new file mode 100644
index 0000000000000000000000000000000000000000..9a644005c7b38fd64597c1eadfc6c708973e9a94
Binary files /dev/null and b/asset/dingding.png differ
diff --git a/cosyvoice/__init__.py b/cosyvoice/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/cosyvoice/bin/inference.py b/cosyvoice/bin/inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b777fa1cba925f9786db60b7efa15dcd189adeb
--- /dev/null
+++ b/cosyvoice/bin/inference.py
@@ -0,0 +1,114 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import argparse
+import logging
+logging.getLogger('matplotlib').setLevel(logging.WARNING)
+import os
+
+import torch
+from torch.utils.data import DataLoader
+import torchaudio
+from hyperpyyaml import load_hyperpyyaml
+from tqdm import tqdm
+from cosyvoice.cli.model import CosyVoiceModel
+
+from cosyvoice.dataset.dataset import Dataset
+
+def get_args():
+ parser = argparse.ArgumentParser(description='inference with your model')
+ parser.add_argument('--config', required=True, help='config file')
+ parser.add_argument('--prompt_data', required=True, help='prompt data file')
+ parser.add_argument('--prompt_utt2data', required=True, help='prompt data file')
+ parser.add_argument('--tts_text', required=True, help='tts input file')
+ parser.add_argument('--llm_model', required=True, help='llm model file')
+ parser.add_argument('--flow_model', required=True, help='flow model file')
+ parser.add_argument('--hifigan_model', required=True, help='hifigan model file')
+ parser.add_argument('--gpu',
+ type=int,
+ default=-1,
+ help='gpu id for this rank, -1 for cpu')
+ parser.add_argument('--mode',
+ default='sft',
+ choices=['sft', 'zero_shot'],
+ help='inference mode')
+ parser.add_argument('--result_dir', required=True, help='asr result file')
+ args = parser.parse_args()
+ print(args)
+ return args
+
+
+def main():
+ args = get_args()
+ logging.basicConfig(level=logging.DEBUG,
+ format='%(asctime)s %(levelname)s %(message)s')
+ os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
+
+ # Init cosyvoice models from configs
+ use_cuda = args.gpu >= 0 and torch.cuda.is_available()
+ device = torch.device('cuda' if use_cuda else 'cpu')
+ with open(args.config, 'r') as f:
+ configs = load_hyperpyyaml(f)
+
+ model = CosyVoiceModel(configs['llm'], configs['flow'], configs['hift'])
+ model.load(args.llm_model, args.flow_model, args.hifigan_model)
+
+ test_dataset = Dataset(args.prompt_data, data_pipeline=configs['data_pipeline'], mode='inference', shuffle=False, partition=False, tts_file=args.tts_text, prompt_utt2data=args.prompt_utt2data)
+ test_data_loader = DataLoader(test_dataset, batch_size=None, num_workers=0)
+
+ del configs
+ os.makedirs(args.result_dir, exist_ok=True)
+ fn = os.path.join(args.result_dir, 'wav.scp')
+ f = open(fn, 'w')
+ with torch.no_grad():
+ for batch_idx, batch in tqdm(enumerate(test_data_loader)):
+ utts = batch["utts"]
+ assert len(utts) == 1, "inference mode only support batchsize 1"
+ text = batch["text"]
+ text_token = batch["text_token"].to(device)
+ text_token_len = batch["text_token_len"].to(device)
+ tts_text = batch["tts_text"]
+ tts_index = batch["tts_index"]
+ tts_text_token = batch["tts_text_token"].to(device)
+ tts_text_token_len = batch["tts_text_token_len"].to(device)
+ speech_token = batch["speech_token"].to(device)
+ speech_token_len = batch["speech_token_len"].to(device)
+ speech_feat = batch["speech_feat"].to(device)
+ speech_feat_len = batch["speech_feat_len"].to(device)
+ utt_embedding = batch["utt_embedding"].to(device)
+ spk_embedding = batch["spk_embedding"].to(device)
+ if args.mode == 'sft':
+ model_input = {'text': tts_text_token, 'text_len': tts_text_token_len,
+ 'llm_embedding': spk_embedding, 'flow_embedding': spk_embedding}
+ else:
+ model_input = {'text': tts_text_token, 'text_len': tts_text_token_len,
+ 'prompt_text': text_token, 'prompt_text_len': text_token_len,
+ 'llm_prompt_speech_token': speech_token, 'llm_prompt_speech_token_len': speech_token_len,
+ 'flow_prompt_speech_token': speech_token, 'flow_prompt_speech_token_len': speech_token_len,
+ 'prompt_speech_feat': speech_feat, 'prompt_speech_feat_len': speech_feat_len,
+ 'llm_embedding': utt_embedding, 'flow_embedding': utt_embedding}
+ model_output = model.inference(**model_input)
+ tts_key = '{}_{}'.format(utts[0], tts_index[0])
+ tts_fn = os.path.join(args.result_dir, '{}.wav'.format(tts_key))
+ torchaudio.save(tts_fn, model_output['tts_speech'], sample_rate=22050)
+ f.write('{} {}\n'.format(tts_key, tts_fn))
+ f.flush()
+ f.close()
+ logging.info('Result wav.scp saved in {}'.format(fn))
+
+
+if __name__ == '__main__':
+ main()
diff --git a/cosyvoice/bin/train.py b/cosyvoice/bin/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9d0e0581d81a8964683dea4af2fd0f407eab5e8
--- /dev/null
+++ b/cosyvoice/bin/train.py
@@ -0,0 +1,136 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+import argparse
+import datetime
+import logging
+logging.getLogger('matplotlib').setLevel(logging.WARNING)
+from copy import deepcopy
+import torch
+import torch.distributed as dist
+import deepspeed
+
+from hyperpyyaml import load_hyperpyyaml
+
+from torch.distributed.elastic.multiprocessing.errors import record
+
+from cosyvoice.utils.executor import Executor
+from cosyvoice.utils.train_utils import (
+ init_distributed,
+ init_dataset_and_dataloader,
+ init_optimizer_and_scheduler,
+ init_summarywriter, save_model,
+ wrap_cuda_model, check_modify_and_save_config)
+
+
+def get_args():
+ parser = argparse.ArgumentParser(description='training your network')
+ parser.add_argument('--train_engine',
+ default='torch_ddp',
+ choices=['torch_ddp', 'deepspeed'],
+ help='Engine for paralleled training')
+ parser.add_argument('--model', required=True, help='model which will be trained')
+ parser.add_argument('--config', required=True, help='config file')
+ parser.add_argument('--train_data', required=True, help='train data file')
+ parser.add_argument('--cv_data', required=True, help='cv data file')
+ parser.add_argument('--checkpoint', help='checkpoint model')
+ parser.add_argument('--model_dir', required=True, help='save model dir')
+ parser.add_argument('--tensorboard_dir',
+ default='tensorboard',
+ help='tensorboard log dir')
+ parser.add_argument('--ddp.dist_backend',
+ dest='dist_backend',
+ default='nccl',
+ choices=['nccl', 'gloo'],
+ help='distributed backend')
+ parser.add_argument('--num_workers',
+ default=0,
+ type=int,
+ help='num of subprocess workers for reading')
+ parser.add_argument('--prefetch',
+ default=100,
+ type=int,
+ help='prefetch number')
+ parser.add_argument('--pin_memory',
+ action='store_true',
+ default=False,
+ help='Use pinned memory buffers used for reading')
+ parser.add_argument('--deepspeed.save_states',
+ dest='save_states',
+ default='model_only',
+ choices=['model_only', 'model+optimizer'],
+ help='save model/optimizer states')
+ parser.add_argument('--timeout',
+ default=30,
+ type=int,
+ help='timeout (in seconds) of cosyvoice_join.')
+ parser = deepspeed.add_config_arguments(parser)
+ args = parser.parse_args()
+ return args
+
+
+@record
+def main():
+ args = get_args()
+ logging.basicConfig(level=logging.DEBUG,
+ format='%(asctime)s %(levelname)s %(message)s')
+
+ override_dict = {k: None for k in ['llm', 'flow', 'hift'] if k != args.model}
+ with open(args.config, 'r') as f:
+ configs = load_hyperpyyaml(f, overrides=override_dict)
+ configs['train_conf'].update(vars(args))
+
+ # Init env for ddp
+ init_distributed(args)
+
+ # Get dataset & dataloader
+ train_dataset, cv_dataset, train_data_loader, cv_data_loader = \
+ init_dataset_and_dataloader(args, configs)
+
+ # Do some sanity checks and save config to arsg.model_dir
+ configs = check_modify_and_save_config(args, configs)
+
+ # Tensorboard summary
+ writer = init_summarywriter(args)
+
+ # load checkpoint
+ model = configs[args.model]
+ if args.checkpoint is not None:
+ model.load_state_dict(torch.load(args.checkpoint, map_location='cpu'))
+
+ # Dispatch model from cpu to gpu
+ model = wrap_cuda_model(args, model)
+
+ # Get optimizer & scheduler
+ model, optimizer, scheduler = init_optimizer_and_scheduler(args, configs, model)
+
+ # Save init checkpoints
+ info_dict = deepcopy(configs['train_conf'])
+ save_model(model, 'init', info_dict)
+
+ # Get executor
+ executor = Executor()
+
+ # Start training loop
+ for epoch in range(info_dict['max_epoch']):
+ executor.epoch = epoch
+ train_dataset.set_epoch(epoch)
+ dist.barrier()
+ group_join = dist.new_group(backend="gloo", timeout=datetime.timedelta(seconds=args.timeout))
+ executor.train_one_epoc(model, optimizer, scheduler, train_data_loader, cv_data_loader, writer, info_dict, group_join)
+ dist.destroy_process_group(group_join)
+
+if __name__ == '__main__':
+ main()
diff --git a/cosyvoice/cli/__init__.py b/cosyvoice/cli/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/cosyvoice/cli/cosyvoice.py b/cosyvoice/cli/cosyvoice.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea8c4482891a62df6cbac39faa88972c81f5412f
--- /dev/null
+++ b/cosyvoice/cli/cosyvoice.py
@@ -0,0 +1,83 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+import torch
+from hyperpyyaml import load_hyperpyyaml
+from modelscope import snapshot_download
+from cosyvoice.cli.frontend import CosyVoiceFrontEnd
+from cosyvoice.cli.model import CosyVoiceModel
+
+class CosyVoice:
+
+ def __init__(self, model_dir):
+ instruct = True if '-Instruct' in model_dir else False
+ self.model_dir = model_dir
+ if not os.path.exists(model_dir):
+ model_dir = snapshot_download(model_dir)
+ with open('{}/cosyvoice.yaml'.format(model_dir), 'r') as f:
+ configs = load_hyperpyyaml(f)
+ self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'],
+ configs['feat_extractor'],
+ '{}/campplus.onnx'.format(model_dir),
+ '{}/speech_tokenizer_v1.onnx'.format(model_dir),
+ '{}/spk2info.pt'.format(model_dir),
+ instruct,
+ configs['allowed_special'])
+ self.model = CosyVoiceModel(configs['llm'], configs['flow'], configs['hift'])
+ self.model.load('{}/llm.pt'.format(model_dir),
+ '{}/flow.pt'.format(model_dir),
+ '{}/hift.pt'.format(model_dir))
+ del configs
+
+ def list_avaliable_spks(self):
+ spks = list(self.frontend.spk2info.keys())
+ return spks
+
+ def inference_sft(self, tts_text, spk_id):
+ tts_speeches = []
+ for i in self.frontend.text_normalize(tts_text, split=True):
+ model_input = self.frontend.frontend_sft(i, spk_id)
+ model_output = self.model.inference(**model_input)
+ tts_speeches.append(model_output['tts_speech'])
+ return {'tts_speech': torch.concat(tts_speeches, dim=1)}
+
+ def inference_zero_shot(self, tts_text, prompt_text, prompt_speech_16k):
+ prompt_text = self.frontend.text_normalize(prompt_text, split=False)
+ tts_speeches = []
+ for i in self.frontend.text_normalize(tts_text, split=True):
+ model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_speech_16k)
+ model_output = self.model.inference(**model_input)
+ tts_speeches.append(model_output['tts_speech'])
+ return {'tts_speech': torch.concat(tts_speeches, dim=1)}
+
+ def inference_cross_lingual(self, tts_text, prompt_speech_16k):
+ if self.frontend.instruct is True:
+ raise ValueError('{} do not support cross_lingual inference'.format(self.model_dir))
+ tts_speeches = []
+ for i in self.frontend.text_normalize(tts_text, split=True):
+ model_input = self.frontend.frontend_cross_lingual(i, prompt_speech_16k)
+ model_output = self.model.inference(**model_input)
+ tts_speeches.append(model_output['tts_speech'])
+ return {'tts_speech': torch.concat(tts_speeches, dim=1)}
+
+ def inference_instruct(self, tts_text, spk_id, instruct_text):
+ if self.frontend.instruct is False:
+ raise ValueError('{} do not support instruct inference'.format(self.model_dir))
+ instruct_text = self.frontend.text_normalize(instruct_text, split=False)
+ tts_speeches = []
+ for i in self.frontend.text_normalize(tts_text, split=True):
+ model_input = self.frontend.frontend_instruct(i, spk_id, instruct_text)
+ model_output = self.model.inference(**model_input)
+ tts_speeches.append(model_output['tts_speech'])
+ return {'tts_speech': torch.concat(tts_speeches, dim=1)}
diff --git a/cosyvoice/cli/frontend.py b/cosyvoice/cli/frontend.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ed85500cd3ab65f8f4f7540c084adb0c648186f
--- /dev/null
+++ b/cosyvoice/cli/frontend.py
@@ -0,0 +1,168 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from functools import partial
+import onnxruntime
+import torch
+import numpy as np
+import whisper
+from typing import Callable
+import torchaudio.compliance.kaldi as kaldi
+import torchaudio
+import os
+import re
+import inflect
+try:
+ import ttsfrd
+ use_ttsfrd = True
+except ImportError:
+ print("failed to import ttsfrd, use WeTextProcessing instead")
+ from tn.chinese.normalizer import Normalizer as ZhNormalizer
+ from tn.english.normalizer import Normalizer as EnNormalizer
+ use_ttsfrd = False
+from cosyvoice.utils.frontend_utils import contains_chinese, replace_blank, replace_corner_mark, remove_bracket, spell_out_number, split_paragraph
+
+
+class CosyVoiceFrontEnd:
+
+ def __init__(self,
+ get_tokenizer: Callable,
+ feat_extractor: Callable,
+ campplus_model: str,
+ speech_tokenizer_model: str,
+ spk2info: str = '',
+ instruct: bool = False,
+ allowed_special: str = 'all'):
+ self.tokenizer = get_tokenizer()
+ self.feat_extractor = feat_extractor
+ self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ option = onnxruntime.SessionOptions()
+ option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
+ option.intra_op_num_threads = 1
+ self.campplus_session = onnxruntime.InferenceSession(campplus_model, sess_options=option, providers=["CPUExecutionProvider"])
+ self.speech_tokenizer_session = onnxruntime.InferenceSession(speech_tokenizer_model, sess_options=option, providers=["CUDAExecutionProvider"if torch.cuda.is_available() else "CPUExecutionProvider"])
+ if os.path.exists(spk2info):
+ self.spk2info = torch.load(spk2info, map_location=self.device)
+ self.instruct = instruct
+ self.allowed_special = allowed_special
+ self.inflect_parser = inflect.engine()
+ self.use_ttsfrd = use_ttsfrd
+ if self.use_ttsfrd:
+ self.frd = ttsfrd.TtsFrontendEngine()
+ ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
+ assert self.frd.initialize('{}/../../pretrained_models/CosyVoice-ttsfrd/resource'.format(ROOT_DIR)) is True, 'failed to initialize ttsfrd resource'
+ self.frd.set_lang_type('pinyin')
+ self.frd.enable_pinyin_mix(True)
+ self.frd.set_breakmodel_index(1)
+ else:
+ self.zh_tn_model = ZhNormalizer(remove_erhua=False, full_to_half=False)
+ self.en_tn_model = EnNormalizer()
+
+ def _extract_text_token(self, text):
+ text_token = self.tokenizer.encode(text, allowed_special=self.allowed_special)
+ text_token = torch.tensor([text_token], dtype=torch.int32).to(self.device)
+ text_token_len = torch.tensor([text_token.shape[1]], dtype=torch.int32).to(self.device)
+ return text_token, text_token_len
+
+ def _extract_speech_token(self, speech):
+ feat = whisper.log_mel_spectrogram(speech, n_mels=128)
+ speech_token = self.speech_tokenizer_session.run(None, {self.speech_tokenizer_session.get_inputs()[0].name: feat.detach().cpu().numpy(),
+ self.speech_tokenizer_session.get_inputs()[1].name: np.array([feat.shape[2]], dtype=np.int32)})[0].flatten().tolist()
+ speech_token = torch.tensor([speech_token], dtype=torch.int32).to(self.device)
+ speech_token_len = torch.tensor([speech_token.shape[1]], dtype=torch.int32).to(self.device)
+ return speech_token, speech_token_len
+
+ def _extract_spk_embedding(self, speech):
+ feat = kaldi.fbank(speech,
+ num_mel_bins=80,
+ dither=0,
+ sample_frequency=16000)
+ feat = feat - feat.mean(dim=0, keepdim=True)
+ embedding = self.campplus_session.run(None, {self.campplus_session.get_inputs()[0].name: feat.unsqueeze(dim=0).cpu().numpy()})[0].flatten().tolist()
+ embedding = torch.tensor([embedding]).to(self.device)
+ return embedding
+
+ def _extract_speech_feat(self, speech):
+ speech_feat = self.feat_extractor(speech).squeeze(dim=0).transpose(0, 1).to(self.device)
+ speech_feat = speech_feat.unsqueeze(dim=0)
+ speech_feat_len = torch.tensor([speech_feat.shape[1]], dtype=torch.int32).to(self.device)
+ return speech_feat, speech_feat_len
+
+ def text_normalize(self, text, split=True):
+ text = text.strip()
+ if contains_chinese(text):
+ if self.use_ttsfrd:
+ text = self.frd.get_frd_extra_info(text, 'input')
+ else:
+ text = self.zh_tn_model.normalize(text)
+ text = text.replace("\n", "")
+ text = replace_blank(text)
+ text = replace_corner_mark(text)
+ text = text.replace(".", "、")
+ text = text.replace(" - ", "，")
+ text = remove_bracket(text)
+ text = re.sub(r'[，,]+$', '。', text)
+ texts = [i for i in split_paragraph(text, partial(self.tokenizer.encode, allowed_special=self.allowed_special), "zh", token_max_n=80,
+ token_min_n=60, merge_len=20,
+ comma_split=False)]
+ else:
+ if self.use_ttsfrd:
+ text = self.frd.get_frd_extra_info(text, 'input')
+ else:
+ text = self.en_tn_model.normalize(text)
+ text = spell_out_number(text, self.inflect_parser)
+ texts = [i for i in split_paragraph(text, partial(self.tokenizer.encode, allowed_special=self.allowed_special), "en", token_max_n=80,
+ token_min_n=60, merge_len=20,
+ comma_split=False)]
+ if split is False:
+ return text
+ return texts
+
+ def frontend_sft(self, tts_text, spk_id):
+ tts_text_token, tts_text_token_len = self._extract_text_token(tts_text)
+ embedding = self.spk2info[spk_id]['embedding']
+ model_input = {'text': tts_text_token, 'text_len': tts_text_token_len, 'llm_embedding': embedding, 'flow_embedding': embedding}
+ return model_input
+
+ def frontend_zero_shot(self, tts_text, prompt_text, prompt_speech_16k):
+ tts_text_token, tts_text_token_len = self._extract_text_token(tts_text)
+ prompt_text_token, prompt_text_token_len = self._extract_text_token(prompt_text)
+ prompt_speech_22050 = torchaudio.transforms.Resample(orig_freq=16000, new_freq=22050)(prompt_speech_16k)
+ speech_feat, speech_feat_len = self._extract_speech_feat(prompt_speech_22050)
+ speech_token, speech_token_len = self._extract_speech_token(prompt_speech_16k)
+ embedding = self._extract_spk_embedding(prompt_speech_16k)
+ model_input = {'text': tts_text_token, 'text_len': tts_text_token_len,
+ 'prompt_text': prompt_text_token, 'prompt_text_len': prompt_text_token_len,
+ 'llm_prompt_speech_token': speech_token, 'llm_prompt_speech_token_len': speech_token_len,
+ 'flow_prompt_speech_token': speech_token, 'flow_prompt_speech_token_len': speech_token_len,
+ 'prompt_speech_feat': speech_feat, 'prompt_speech_feat_len': speech_feat_len,
+ 'llm_embedding': embedding, 'flow_embedding': embedding}
+ return model_input
+
+ def frontend_cross_lingual(self, tts_text, prompt_speech_16k):
+ model_input = self.frontend_zero_shot(tts_text, '', prompt_speech_16k)
+ # in cross lingual mode, we remove prompt in llm
+ del model_input['prompt_text']
+ del model_input['prompt_text_len']
+ del model_input['llm_prompt_speech_token']
+ del model_input['llm_prompt_speech_token_len']
+ return model_input
+
+ def frontend_instruct(self, tts_text, spk_id, instruct_text):
+ model_input = self.frontend_sft(tts_text, spk_id)
+ # in instruct mode, we remove spk_embedding in llm due to information leakage
+ del model_input['llm_embedding']
+ instruct_text_token, instruct_text_token_len = self._extract_text_token(instruct_text + '
+
+# 🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching
+
+### [Shivam Mehta](https://www.kth.se/profile/smehta), [Ruibo Tu](https://www.kth.se/profile/ruibo), [Jonas Beskow](https://www.kth.se/profile/beskow), [Éva Székely](https://www.kth.se/profile/szekely), and [Gustav Eje Henter](https://people.kth.se/~ghe/)
+
+[](https://www.python.org/downloads/release/python-3100/)
+[](https://pytorch.org/get-started/locally/)
+[](https://pytorchlightning.ai/)
+[](https://hydra.cc/)
+[](https://black.readthedocs.io/en/stable/)
+[](https://pycqa.github.io/isort/)
+
+
+
+> This is the official code implementation of 🍵 Matcha-TTS [ICASSP 2024].
+
+We propose 🍵 Matcha-TTS, a new approach to non-autoregressive neural TTS, that uses [conditional flow matching](https://arxiv.org/abs/2210.02747) (similar to [rectified flows](https://arxiv.org/abs/2209.03003)) to speed up ODE-based speech synthesis. Our method:
+
+- Is probabilistic
+- Has compact memory footprint
+- Sounds highly natural
+- Is very fast to synthesise from
+
+Check out our [demo page](https://shivammehta25.github.io/Matcha-TTS) and read [our ICASSP 2024 paper](https://arxiv.org/abs/2309.03199) for more details.
+
+[Pre-trained models](https://drive.google.com/drive/folders/17C_gYgEHOxI5ZypcfE_k1piKCtyR0isJ?usp=sharing) will be automatically downloaded with the CLI or gradio interface.
+
+You can also [try 🍵 Matcha-TTS in your browser on HuggingFace 🤗 spaces](https://huggingface.co/spaces/shivammehta25/Matcha-TTS).
+
+## Teaser video
+
+[](https://youtu.be/xmvJkz3bqw0)
+
+## Installation
+
+1. Create an environment (suggested but optional)
+
+```
+conda create -n matcha-tts python=3.10 -y
+conda activate matcha-tts
+```
+
+2. Install Matcha TTS using pip or from source
+
+```bash
+pip install matcha-tts
+```
+
+from source
+
+```bash
+pip install git+https://github.com/shivammehta25/Matcha-TTS.git
+cd Matcha-TTS
+pip install -e .
+```
+
+3. Run CLI / gradio app / jupyter notebook
+
+```bash
+# This will download the required models
+matcha-tts --text ""
+```
+
+or
+
+```bash
+matcha-tts-app
+```
+
+or open `synthesis.ipynb` on jupyter notebook
+
+### CLI Arguments
+
+- To synthesise from given text, run:
+
+```bash
+matcha-tts --text ""
+```
+
+- To synthesise from a file, run:
+
+```bash
+matcha-tts --file
+ 
+
' + gr.HTML(html) + + with gr.Box(): + radio_options = list(RADIO_OPTIONS.keys()) + model_type = gr.Radio( + radio_options, value=radio_options[0], label="Choose a Model", interactive=True, container=False + ) + + with gr.Row(): + gr.Markdown("# Text Input") + with gr.Row(): + text = gr.Textbox(value="", lines=2, label="Text to synthesise", scale=3) + spk_slider = gr.Slider( + minimum=0, maximum=107, step=1, value=args.spk, label="Speaker ID", interactive=True, scale=1 + ) + + with gr.Row(): + gr.Markdown("### Hyper parameters") + with gr.Row(): + n_timesteps = gr.Slider( + label="Number of ODE steps", + minimum=1, + maximum=100, + step=1, + value=10, + interactive=True, + ) + length_scale = gr.Slider( + label="Length scale (Speaking rate)", + minimum=0.5, + maximum=1.5, + step=0.05, + value=1.0, + interactive=True, + ) + mel_temp = gr.Slider( + label="Sampling temperature", + minimum=0.00, + maximum=2.001, + step=0.16675, + value=0.667, + interactive=True, + ) + + synth_btn = gr.Button("Synthesise") + + with gr.Box(): + with gr.Row(): + gr.Markdown("### Phonetised text") + phonetised_text = gr.Textbox(interactive=False, scale=10, label="Phonetised text") + + with gr.Box(): + with gr.Row(): + mel_spectrogram = gr.Image(interactive=False, label="mel spectrogram") + + # with gr.Row(): + audio = gr.Audio(interactive=False, label="Audio") + + with gr.Row(visible=False) as example_row_lj_speech: + examples = gr.Examples( # pylint: disable=unused-variable + examples=[ + [ + "We propose Matcha-TTS, a new approach to non-autoregressive neural TTS, that uses conditional flow matching (similar to rectified flows) to speed up O D E-based speech synthesis.", + 50, + 0.677, + 0.95, + ], + [ + "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.", + 2, + 0.677, + 0.95, + ], + [ + "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.", + 4, + 0.677, + 0.95, + ], + [ + "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.", + 10, + 0.677, + 0.95, + ], + [ + "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.", + 50, + 0.677, + 0.95, + ], + [ + "The narrative of these events is based largely on the recollections of the participants.", + 10, + 0.677, + 0.95, + ], + [ + "The jury did not believe him, and the verdict was for the defendants.", + 10, + 0.677, + 0.95, + ], + ], + fn=ljspeech_example_cacher, + inputs=[text, n_timesteps, mel_temp, length_scale], + outputs=[phonetised_text, audio, mel_spectrogram], + cache_examples=True, + ) + + with gr.Row() as example_row_multispeaker: + multi_speaker_examples = gr.Examples( # pylint: disable=unused-variable + examples=[ + [ + "Hello everyone! I am speaker 0 and I am here to tell you that Matcha-TTS is amazing!", + 10, + 0.677, + 0.85, + 0, + ], + [ + "Hello everyone! I am speaker 16 and I am here to tell you that Matcha-TTS is amazing!", + 10, + 0.677, + 0.85, + 16, + ], + [ + "Hello everyone! I am speaker 44 and I am here to tell you that Matcha-TTS is amazing!", + 50, + 0.677, + 0.85, + 44, + ], + [ + "Hello everyone! I am speaker 45 and I am here to tell you that Matcha-TTS is amazing!", + 50, + 0.677, + 0.85, + 45, + ], + [ + "Hello everyone! I am speaker 58 and I am here to tell you that Matcha-TTS is amazing!", + 4, + 0.677, + 0.85, + 58, + ], + ], + fn=multispeaker_example_cacher, + inputs=[text, n_timesteps, mel_temp, length_scale, spk_slider], + outputs=[phonetised_text, audio, mel_spectrogram], + cache_examples=True, + label="Multi Speaker Examples", + ) + + model_type.change(lambda x: gr.update(interactive=False), inputs=[synth_btn], outputs=[synth_btn]).then( + load_model_ui, + inputs=[model_type, text], + outputs=[text, synth_btn, spk_slider, example_row_lj_speech, example_row_multispeaker, length_scale], + ) + + synth_btn.click( + fn=process_text_gradio, + inputs=[ + text, + ], + outputs=[phonetised_text, processed_text, processed_text_len], + api_name="matcha_tts", + queue=True, + ).then( + fn=synthesise_mel, + inputs=[processed_text, processed_text_len, n_timesteps, mel_temp, length_scale, spk_slider], + outputs=[audio, mel_spectrogram], + ) + + demo.queue().launch(share=True) + + +if __name__ == "__main__": + main() diff --git a/third_party/Matcha-TTS/matcha/cli.py b/third_party/Matcha-TTS/matcha/cli.py new file mode 100644 index 0000000000000000000000000000000000000000..579d7d636450a41f1c06a4393d64ddbae38c5011 --- /dev/null +++ b/third_party/Matcha-TTS/matcha/cli.py @@ -0,0 +1,418 @@ +import argparse +import datetime as dt +import os +import warnings +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import soundfile as sf +import torch + +from matcha.hifigan.config import v1 +from matcha.hifigan.denoiser import Denoiser +from matcha.hifigan.env import AttrDict +from matcha.hifigan.models import Generator as HiFiGAN +from matcha.models.matcha_tts import MatchaTTS +from matcha.text import sequence_to_text, text_to_sequence +from matcha.utils.utils import assert_model_downloaded, get_user_data_dir, intersperse + +MATCHA_URLS = { + "matcha_ljspeech": "https://github.com/shivammehta25/Matcha-TTS-checkpoints/releases/download/v1.0/matcha_ljspeech.ckpt", + "matcha_vctk": "https://github.com/shivammehta25/Matcha-TTS-checkpoints/releases/download/v1.0/matcha_vctk.ckpt", +} + +VOCODER_URLS = { + "hifigan_T2_v1": "https://github.com/shivammehta25/Matcha-TTS-checkpoints/releases/download/v1.0/generator_v1", # Old url: https://drive.google.com/file/d/14NENd4equCBLyyCSke114Mv6YR_j_uFs/view?usp=drive_link + "hifigan_univ_v1": "https://github.com/shivammehta25/Matcha-TTS-checkpoints/releases/download/v1.0/g_02500000", # Old url: https://drive.google.com/file/d/1qpgI41wNXFcH-iKq1Y42JlBC9j0je8PW/view?usp=drive_link +} + +MULTISPEAKER_MODEL = { + "matcha_vctk": {"vocoder": "hifigan_univ_v1", "speaking_rate": 0.85, "spk": 0, "spk_range": (0, 107)} +} + +SINGLESPEAKER_MODEL = {"matcha_ljspeech": {"vocoder": "hifigan_T2_v1", "speaking_rate": 0.95, "spk": None}} + + +def plot_spectrogram_to_numpy(spectrogram, filename): + fig, ax = plt.subplots(figsize=(12, 3)) + im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none") + plt.colorbar(im, ax=ax) + plt.xlabel("Frames") + plt.ylabel("Channels") + plt.title("Synthesised Mel-Spectrogram") + fig.canvas.draw() + plt.savefig(filename) + + +def process_text(i: int, text: str, device: torch.device): + print(f"[{i}] - Input text: {text}") + x = torch.tensor( + intersperse(text_to_sequence(text, ["english_cleaners2"]), 0), + dtype=torch.long, + device=device, + )[None] + x_lengths = torch.tensor([x.shape[-1]], dtype=torch.long, device=device) + x_phones = sequence_to_text(x.squeeze(0).tolist()) + print(f"[{i}] - Phonetised text: {x_phones[1::2]}") + + return {"x_orig": text, "x": x, "x_lengths": x_lengths, "x_phones": x_phones} + + +def get_texts(args): + if args.text: + texts = [args.text] + else: + with open(args.file, encoding="utf-8") as f: + texts = f.readlines() + return texts + + +def assert_required_models_available(args): + save_dir = get_user_data_dir() + if not hasattr(args, "checkpoint_path") and args.checkpoint_path is None: + model_path = args.checkpoint_path + else: + model_path = save_dir / f"{args.model}.ckpt" + assert_model_downloaded(model_path, MATCHA_URLS[args.model]) + + vocoder_path = save_dir / f"{args.vocoder}" + assert_model_downloaded(vocoder_path, VOCODER_URLS[args.vocoder]) + return {"matcha": model_path, "vocoder": vocoder_path} + + +def load_hifigan(checkpoint_path, device): + h = AttrDict(v1) + hifigan = HiFiGAN(h).to(device) + hifigan.load_state_dict(torch.load(checkpoint_path, map_location=device)["generator"]) + _ = hifigan.eval() + hifigan.remove_weight_norm() + return hifigan + + +def load_vocoder(vocoder_name, checkpoint_path, device): + print(f"[!] Loading {vocoder_name}!") + vocoder = None + if vocoder_name in ("hifigan_T2_v1", "hifigan_univ_v1"): + vocoder = load_hifigan(checkpoint_path, device) + else: + raise NotImplementedError( + f"Vocoder {vocoder_name} not implemented! define a load_<
+We provide our implementation and pretrained models as open source in this repository. + +**Abstract :** +Several recent work on speech synthesis have employed generative adversarial networks (GANs) to produce raw waveforms. +Although such methods improve the sampling efficiency and memory usage, +their sample quality has not yet reached that of autoregressive and flow-based generative models. +In this work, we propose HiFi-GAN, which achieves both efficient and high-fidelity speech synthesis. +As speech audio consists of sinusoidal signals with various periods, +we demonstrate that modeling periodic patterns of an audio is crucial for enhancing sample quality. +A subjective human evaluation (mean opinion score, MOS) of a single speaker dataset indicates that our proposed method +demonstrates similarity to human quality while generating 22.05 kHz high-fidelity audio 167.9 times faster than +real-time on a single V100 GPU. We further show the generality of HiFi-GAN to the mel-spectrogram inversion of unseen +speakers and end-to-end speech synthesis. Finally, a small footprint version of HiFi-GAN generates samples 13.4 times +faster than real-time on CPU with comparable quality to an autoregressive counterpart. + +Visit our [demo website](https://jik876.github.io/hifi-gan-demo/) for audio samples. + +## Pre-requisites + +1. Python >= 3.6 +2. Clone this repository. +3. Install python requirements. Please refer [requirements.txt](requirements.txt) +4. Download and extract the [LJ Speech dataset](https://keithito.com/LJ-Speech-Dataset/). + And move all wav files to `LJSpeech-1.1/wavs` + +## Training + +``` +python train.py --config config_v1.json +``` + +To train V2 or V3 Generator, replace `config_v1.json` with `config_v2.json` or `config_v3.json`.
+Checkpoints and copy of the configuration file are saved in `cp_hifigan` directory by default.
+You can change the path by adding `--checkpoint_path` option. + +Validation loss during training with V1 generator.
+ + +## Pretrained Model + +You can also use pretrained models we provide.
+[Download pretrained models](https://drive.google.com/drive/folders/1-eEYTB5Av9jNql0WGBlRoi-WH2J7bp5Y?usp=sharing)
+Details of each folder are as in follows: + +| Folder Name | Generator | Dataset | Fine-Tuned | +| ------------ | --------- | --------- | ------------------------------------------------------ | +| LJ_V1 | V1 | LJSpeech | No | +| LJ_V2 | V2 | LJSpeech | No | +| LJ_V3 | V3 | LJSpeech | No | +| LJ_FT_T2_V1 | V1 | LJSpeech | Yes ([Tacotron2](https://github.com/NVIDIA/tacotron2)) | +| LJ_FT_T2_V2 | V2 | LJSpeech | Yes ([Tacotron2](https://github.com/NVIDIA/tacotron2)) | +| LJ_FT_T2_V3 | V3 | LJSpeech | Yes ([Tacotron2](https://github.com/NVIDIA/tacotron2)) | +| VCTK_V1 | V1 | VCTK | No | +| VCTK_V2 | V2 | VCTK | No | +| VCTK_V3 | V3 | VCTK | No | +| UNIVERSAL_V1 | V1 | Universal | No | + +We provide the universal model with discriminator weights that can be used as a base for transfer learning to other datasets. + +## Fine-Tuning + +1. Generate mel-spectrograms in numpy format using [Tacotron2](https://github.com/NVIDIA/tacotron2) with teacher-forcing.
+ The file name of the generated mel-spectrogram should match the audio file and the extension should be `.npy`.
+ Example: + ` Audio File : LJ001-0001.wav +Mel-Spectrogram File : LJ001-0001.npy` +2. Create `ft_dataset` folder and copy the generated mel-spectrogram files into it.
+3. Run the following command. + ``` + python train.py --fine_tuning True --config config_v1.json + ``` + For other command line options, please refer to the training section. + +## Inference from wav file + +1. Make `test_files` directory and copy wav files into the directory. +2. Run the following command. + ` python inference.py --checkpoint_file [generator checkpoint file path]` + Generated wav files are saved in `generated_files` by default.
+ You can change the path by adding `--output_dir` option. + +## Inference for end-to-end speech synthesis + +1. Make `test_mel_files` directory and copy generated mel-spectrogram files into the directory.
+ You can generate mel-spectrograms using [Tacotron2](https://github.com/NVIDIA/tacotron2), + [Glow-TTS](https://github.com/jaywalnut310/glow-tts) and so forth. +2. Run the following command. + ` python inference_e2e.py --checkpoint_file [generator checkpoint file path]` + Generated wav files are saved in `generated_files_from_mel` by default.
+ You can change the path by adding `--output_dir` option. + +## Acknowledgements + +We referred to [WaveGlow](https://github.com/NVIDIA/waveglow), [MelGAN](https://github.com/descriptinc/melgan-neurips) +and [Tacotron2](https://github.com/NVIDIA/tacotron2) to implement this. diff --git a/third_party/Matcha-TTS/matcha/hifigan/__init__.py b/third_party/Matcha-TTS/matcha/hifigan/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/Matcha-TTS/matcha/hifigan/config.py b/third_party/Matcha-TTS/matcha/hifigan/config.py new file mode 100644 index 0000000000000000000000000000000000000000..b3abea9e151a08864353d32066bd4935e24b82e7 --- /dev/null +++ b/third_party/Matcha-TTS/matcha/hifigan/config.py @@ -0,0 +1,28 @@ +v1 = { + "resblock": "1", + "num_gpus": 0, + "batch_size": 16, + "learning_rate": 0.0004, + "adam_b1": 0.8, + "adam_b2": 0.99, + "lr_decay": 0.999, + "seed": 1234, + "upsample_rates": [8, 8, 2, 2], + "upsample_kernel_sizes": [16, 16, 4, 4], + "upsample_initial_channel": 512, + "resblock_kernel_sizes": [3, 7, 11], + "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]], + "resblock_initial_channel": 256, + "segment_size": 8192, + "num_mels": 80, + "num_freq": 1025, + "n_fft": 1024, + "hop_size": 256, + "win_size": 1024, + "sampling_rate": 22050, + "fmin": 0, + "fmax": 8000, + "fmax_loss": None, + "num_workers": 4, + "dist_config": {"dist_backend": "nccl", "dist_url": "tcp://localhost:54321", "world_size": 1}, +} diff --git a/third_party/Matcha-TTS/matcha/hifigan/denoiser.py b/third_party/Matcha-TTS/matcha/hifigan/denoiser.py new file mode 100644 index 0000000000000000000000000000000000000000..9fd33312a09b1940374a0e29a97fe3a1a1dac7d2 --- /dev/null +++ b/third_party/Matcha-TTS/matcha/hifigan/denoiser.py @@ -0,0 +1,64 @@ +# Code modified from Rafael Valle's implementation https://github.com/NVIDIA/waveglow/blob/5bc2a53e20b3b533362f974cfa1ea0267ae1c2b1/denoiser.py + +"""Waveglow style denoiser can be used to remove the artifacts from the HiFiGAN generated audio.""" +import torch + + +class Denoiser(torch.nn.Module): + """Removes model bias from audio produced with waveglow""" + + def __init__(self, vocoder, filter_length=1024, n_overlap=4, win_length=1024, mode="zeros"): + super().__init__() + self.filter_length = filter_length + self.hop_length = int(filter_length / n_overlap) + self.win_length = win_length + + dtype, device = next(vocoder.parameters()).dtype, next(vocoder.parameters()).device + self.device = device + if mode == "zeros": + mel_input = torch.zeros((1, 80, 88), dtype=dtype, device=device) + elif mode == "normal": + mel_input = torch.randn((1, 80, 88), dtype=dtype, device=device) + else: + raise Exception(f"Mode {mode} if not supported") + + def stft_fn(audio, n_fft, hop_length, win_length, window): + spec = torch.stft( + audio, + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + window=window, + return_complex=True, + ) + spec = torch.view_as_real(spec) + return torch.sqrt(spec.pow(2).sum(-1)), torch.atan2(spec[..., -1], spec[..., 0]) + + self.stft = lambda x: stft_fn( + audio=x, + n_fft=self.filter_length, + hop_length=self.hop_length, + win_length=self.win_length, + window=torch.hann_window(self.win_length, device=device), + ) + self.istft = lambda x, y: torch.istft( + torch.complex(x * torch.cos(y), x * torch.sin(y)), + n_fft=self.filter_length, + hop_length=self.hop_length, + win_length=self.win_length, + window=torch.hann_window(self.win_length, device=device), + ) + + with torch.no_grad(): + bias_audio = vocoder(mel_input).float().squeeze(0) + bias_spec, _ = self.stft(bias_audio) + + self.register_buffer("bias_spec", bias_spec[:, :, 0][:, :, None]) + + @torch.inference_mode() + def forward(self, audio, strength=0.0005): + audio_spec, audio_angles = self.stft(audio) + audio_spec_denoised = audio_spec - self.bias_spec.to(audio.device) * strength + audio_spec_denoised = torch.clamp(audio_spec_denoised, 0.0) + audio_denoised = self.istft(audio_spec_denoised, audio_angles) + return audio_denoised diff --git a/third_party/Matcha-TTS/matcha/hifigan/env.py b/third_party/Matcha-TTS/matcha/hifigan/env.py new file mode 100644 index 0000000000000000000000000000000000000000..9ea4f948a3f002921bf9bc24f52cbc1c0b1fc2ec --- /dev/null +++ b/third_party/Matcha-TTS/matcha/hifigan/env.py @@ -0,0 +1,17 @@ +""" from https://github.com/jik876/hifi-gan """ + +import os +import shutil + + +class AttrDict(dict): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.__dict__ = self + + +def build_env(config, config_name, path): + t_path = os.path.join(path, config_name) + if config != t_path: + os.makedirs(path, exist_ok=True) + shutil.copyfile(config, os.path.join(path, config_name)) diff --git a/third_party/Matcha-TTS/matcha/hifigan/meldataset.py b/third_party/Matcha-TTS/matcha/hifigan/meldataset.py new file mode 100644 index 0000000000000000000000000000000000000000..8b43ea7965e04a52d5427a485ee911b743057c4a --- /dev/null +++ b/third_party/Matcha-TTS/matcha/hifigan/meldataset.py @@ -0,0 +1,217 @@ +""" from https://github.com/jik876/hifi-gan """ + +import math +import os +import random + +import numpy as np +import torch +import torch.utils.data +from librosa.filters import mel as librosa_mel_fn +from librosa.util import normalize +from scipy.io.wavfile import read + +MAX_WAV_VALUE = 32768.0 + + +def load_wav(full_path): + sampling_rate, data = read(full_path) + return data, sampling_rate + + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) + + +def dynamic_range_decompression(x, C=1): + return np.exp(x) / C + + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def dynamic_range_decompression_torch(x, C=1): + return torch.exp(x) / C + + +def spectral_normalize_torch(magnitudes): + output = dynamic_range_compression_torch(magnitudes) + return output + + +def spectral_de_normalize_torch(magnitudes): + output = dynamic_range_decompression_torch(magnitudes) + return output + + +mel_basis = {} +hann_window = {} + + +def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): + if torch.min(y) < -1.0: + print("min value is ", torch.min(y)) + if torch.max(y) > 1.0: + print("max value is ", torch.max(y)) + + global mel_basis, hann_window # pylint: disable=global-statement + if fmax not in mel_basis: + mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) + mel_basis[str(fmax) + "_" + str(y.device)] = torch.from_numpy(mel).float().to(y.device) + hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device) + + y = torch.nn.functional.pad( + y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect" + ) + y = y.squeeze(1) + + spec = torch.view_as_real( + torch.stft( + y, + n_fft, + hop_length=hop_size, + win_length=win_size, + window=hann_window[str(y.device)], + center=center, + pad_mode="reflect", + normalized=False, + onesided=True, + return_complex=True, + ) + ) + + spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) + + spec = torch.matmul(mel_basis[str(fmax) + "_" + str(y.device)], spec) + spec = spectral_normalize_torch(spec) + + return spec + + +def get_dataset_filelist(a): + with open(a.input_training_file, encoding="utf-8") as fi: + training_files = [ + os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav") for x in fi.read().split("\n") if len(x) > 0 + ] + + with open(a.input_validation_file, encoding="utf-8") as fi: + validation_files = [ + os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav") for x in fi.read().split("\n") if len(x) > 0 + ] + return training_files, validation_files + + +class MelDataset(torch.utils.data.Dataset): + def __init__( + self, + training_files, + segment_size, + n_fft, + num_mels, + hop_size, + win_size, + sampling_rate, + fmin, + fmax, + split=True, + shuffle=True, + n_cache_reuse=1, + device=None, + fmax_loss=None, + fine_tuning=False, + base_mels_path=None, + ): + self.audio_files = training_files + random.seed(1234) + if shuffle: + random.shuffle(self.audio_files) + self.segment_size = segment_size + self.sampling_rate = sampling_rate + self.split = split + self.n_fft = n_fft + self.num_mels = num_mels + self.hop_size = hop_size + self.win_size = win_size + self.fmin = fmin + self.fmax = fmax + self.fmax_loss = fmax_loss + self.cached_wav = None + self.n_cache_reuse = n_cache_reuse + self._cache_ref_count = 0 + self.device = device + self.fine_tuning = fine_tuning + self.base_mels_path = base_mels_path + + def __getitem__(self, index): + filename = self.audio_files[index] + if self._cache_ref_count == 0: + audio, sampling_rate = load_wav(filename) + audio = audio / MAX_WAV_VALUE + if not self.fine_tuning: + audio = normalize(audio) * 0.95 + self.cached_wav = audio + if sampling_rate != self.sampling_rate: + raise ValueError(f"{sampling_rate} SR doesn't match target {self.sampling_rate} SR") + self._cache_ref_count = self.n_cache_reuse + else: + audio = self.cached_wav + self._cache_ref_count -= 1 + + audio = torch.FloatTensor(audio) + audio = audio.unsqueeze(0) + + if not self.fine_tuning: + if self.split: + if audio.size(1) >= self.segment_size: + max_audio_start = audio.size(1) - self.segment_size + audio_start = random.randint(0, max_audio_start) + audio = audio[:, audio_start : audio_start + self.segment_size] + else: + audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), "constant") + + mel = mel_spectrogram( + audio, + self.n_fft, + self.num_mels, + self.sampling_rate, + self.hop_size, + self.win_size, + self.fmin, + self.fmax, + center=False, + ) + else: + mel = np.load(os.path.join(self.base_mels_path, os.path.splitext(os.path.split(filename)[-1])[0] + ".npy")) + mel = torch.from_numpy(mel) + + if len(mel.shape) < 3: + mel = mel.unsqueeze(0) + + if self.split: + frames_per_seg = math.ceil(self.segment_size / self.hop_size) + + if audio.size(1) >= self.segment_size: + mel_start = random.randint(0, mel.size(2) - frames_per_seg - 1) + mel = mel[:, :, mel_start : mel_start + frames_per_seg] + audio = audio[:, mel_start * self.hop_size : (mel_start + frames_per_seg) * self.hop_size] + else: + mel = torch.nn.functional.pad(mel, (0, frames_per_seg - mel.size(2)), "constant") + audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), "constant") + + mel_loss = mel_spectrogram( + audio, + self.n_fft, + self.num_mels, + self.sampling_rate, + self.hop_size, + self.win_size, + self.fmin, + self.fmax_loss, + center=False, + ) + + return (mel.squeeze(), audio.squeeze(0), filename, mel_loss.squeeze()) + + def __len__(self): + return len(self.audio_files) diff --git a/third_party/Matcha-TTS/matcha/hifigan/models.py b/third_party/Matcha-TTS/matcha/hifigan/models.py new file mode 100644 index 0000000000000000000000000000000000000000..d209d9a4e99ec29e4167a5a2eaa62d72b3eff694 --- /dev/null +++ b/third_party/Matcha-TTS/matcha/hifigan/models.py @@ -0,0 +1,368 @@ +""" from https://github.com/jik876/hifi-gan """ + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d +from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm + +from .xutils import get_padding, init_weights + +LRELU_SLOPE = 0.1 + + +class ResBlock1(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)): + super().__init__() + self.h = h + self.convs1 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[2], + padding=get_padding(kernel_size, dilation[2]), + ) + ), + ] + ) + self.convs1.apply(init_weights) + + self.convs2 = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + ), + ] + ) + self.convs2.apply(init_weights) + + def forward(self, x): + for c1, c2 in zip(self.convs1, self.convs2): + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c1(xt) + xt = F.leaky_relu(xt, LRELU_SLOPE) + xt = c2(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs1: + remove_weight_norm(l) + for l in self.convs2: + remove_weight_norm(l) + + +class ResBlock2(torch.nn.Module): + def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)): + super().__init__() + self.h = h + self.convs = nn.ModuleList( + [ + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=get_padding(kernel_size, dilation[0]), + ) + ), + weight_norm( + Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=get_padding(kernel_size, dilation[1]), + ) + ), + ] + ) + self.convs.apply(init_weights) + + def forward(self, x): + for c in self.convs: + xt = F.leaky_relu(x, LRELU_SLOPE) + xt = c(xt) + x = xt + x + return x + + def remove_weight_norm(self): + for l in self.convs: + remove_weight_norm(l) + + +class Generator(torch.nn.Module): + def __init__(self, h): + super().__init__() + self.h = h + self.num_kernels = len(h.resblock_kernel_sizes) + self.num_upsamples = len(h.upsample_rates) + self.conv_pre = weight_norm(Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3)) + resblock = ResBlock1 if h.resblock == "1" else ResBlock2 + + self.ups = nn.ModuleList() + for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)): + self.ups.append( + weight_norm( + ConvTranspose1d( + h.upsample_initial_channel // (2**i), + h.upsample_initial_channel // (2 ** (i + 1)), + k, + u, + padding=(k - u) // 2, + ) + ) + ) + + self.resblocks = nn.ModuleList() + for i in range(len(self.ups)): + ch = h.upsample_initial_channel // (2 ** (i + 1)) + for _, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)): + self.resblocks.append(resblock(h, ch, k, d)) + + self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3)) + self.ups.apply(init_weights) + self.conv_post.apply(init_weights) + + def forward(self, x): + x = self.conv_pre(x) + for i in range(self.num_upsamples): + x = F.leaky_relu(x, LRELU_SLOPE) + x = self.ups[i](x) + xs = None + for j in range(self.num_kernels): + if xs is None: + xs = self.resblocks[i * self.num_kernels + j](x) + else: + xs += self.resblocks[i * self.num_kernels + j](x) + x = xs / self.num_kernels + x = F.leaky_relu(x) + x = self.conv_post(x) + x = torch.tanh(x) + + return x + + def remove_weight_norm(self): + print("Removing weight norm...") + for l in self.ups: + remove_weight_norm(l) + for l in self.resblocks: + l.remove_weight_norm() + remove_weight_norm(self.conv_pre) + remove_weight_norm(self.conv_post) + + +class DiscriminatorP(torch.nn.Module): + def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): + super().__init__() + self.period = period + norm_f = weight_norm if use_spectral_norm is False else spectral_norm + self.convs = nn.ModuleList( + [ + norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))), + norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))), + ] + ) + self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) + + def forward(self, x): + fmap = [] + + # 1d to 2d + b, c, t = x.shape + if t % self.period != 0: # pad first + n_pad = self.period - (t % self.period) + x = F.pad(x, (0, n_pad), "reflect") + t = t + n_pad + x = x.view(b, c, t // self.period, self.period) + + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiPeriodDiscriminator(torch.nn.Module): + def __init__(self): + super().__init__() + self.discriminators = nn.ModuleList( + [ + DiscriminatorP(2), + DiscriminatorP(3), + DiscriminatorP(5), + DiscriminatorP(7), + DiscriminatorP(11), + ] + ) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for _, d in enumerate(self.discriminators): + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +class DiscriminatorS(torch.nn.Module): + def __init__(self, use_spectral_norm=False): + super().__init__() + norm_f = weight_norm if use_spectral_norm is False else spectral_norm + self.convs = nn.ModuleList( + [ + norm_f(Conv1d(1, 128, 15, 1, padding=7)), + norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)), + norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)), + norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)), + norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), + ] + ) + self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) + + def forward(self, x): + fmap = [] + for l in self.convs: + x = l(x) + x = F.leaky_relu(x, LRELU_SLOPE) + fmap.append(x) + x = self.conv_post(x) + fmap.append(x) + x = torch.flatten(x, 1, -1) + + return x, fmap + + +class MultiScaleDiscriminator(torch.nn.Module): + def __init__(self): + super().__init__() + self.discriminators = nn.ModuleList( + [ + DiscriminatorS(use_spectral_norm=True), + DiscriminatorS(), + DiscriminatorS(), + ] + ) + self.meanpools = nn.ModuleList([AvgPool1d(4, 2, padding=2), AvgPool1d(4, 2, padding=2)]) + + def forward(self, y, y_hat): + y_d_rs = [] + y_d_gs = [] + fmap_rs = [] + fmap_gs = [] + for i, d in enumerate(self.discriminators): + if i != 0: + y = self.meanpools[i - 1](y) + y_hat = self.meanpools[i - 1](y_hat) + y_d_r, fmap_r = d(y) + y_d_g, fmap_g = d(y_hat) + y_d_rs.append(y_d_r) + fmap_rs.append(fmap_r) + y_d_gs.append(y_d_g) + fmap_gs.append(fmap_g) + + return y_d_rs, y_d_gs, fmap_rs, fmap_gs + + +def feature_loss(fmap_r, fmap_g): + loss = 0 + for dr, dg in zip(fmap_r, fmap_g): + for rl, gl in zip(dr, dg): + loss += torch.mean(torch.abs(rl - gl)) + + return loss * 2 + + +def discriminator_loss(disc_real_outputs, disc_generated_outputs): + loss = 0 + r_losses = [] + g_losses = [] + for dr, dg in zip(disc_real_outputs, disc_generated_outputs): + r_loss = torch.mean((1 - dr) ** 2) + g_loss = torch.mean(dg**2) + loss += r_loss + g_loss + r_losses.append(r_loss.item()) + g_losses.append(g_loss.item()) + + return loss, r_losses, g_losses + + +def generator_loss(disc_outputs): + loss = 0 + gen_losses = [] + for dg in disc_outputs: + l = torch.mean((1 - dg) ** 2) + gen_losses.append(l) + loss += l + + return loss, gen_losses diff --git a/third_party/Matcha-TTS/matcha/hifigan/xutils.py b/third_party/Matcha-TTS/matcha/hifigan/xutils.py new file mode 100644 index 0000000000000000000000000000000000000000..eefadcb7a1d0bf9015e636b88fee3e22c9771bc5 --- /dev/null +++ b/third_party/Matcha-TTS/matcha/hifigan/xutils.py @@ -0,0 +1,60 @@ +""" from https://github.com/jik876/hifi-gan """ + +import glob +import os + +import matplotlib +import torch +from torch.nn.utils import weight_norm + +matplotlib.use("Agg") +import matplotlib.pylab as plt + + +def plot_spectrogram(spectrogram): + fig, ax = plt.subplots(figsize=(10, 2)) + im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none") + plt.colorbar(im, ax=ax) + + fig.canvas.draw() + plt.close() + + return fig + + +def init_weights(m, mean=0.0, std=0.01): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + m.weight.data.normal_(mean, std) + + +def apply_weight_norm(m): + classname = m.__class__.__name__ + if classname.find("Conv") != -1: + weight_norm(m) + + +def get_padding(kernel_size, dilation=1): + return int((kernel_size * dilation - dilation) / 2) + + +def load_checkpoint(filepath, device): + assert os.path.isfile(filepath) + print(f"Loading '{filepath}'") + checkpoint_dict = torch.load(filepath, map_location=device) + print("Complete.") + return checkpoint_dict + + +def save_checkpoint(filepath, obj): + print(f"Saving checkpoint to {filepath}") + torch.save(obj, filepath) + print("Complete.") + + +def scan_checkpoint(cp_dir, prefix): + pattern = os.path.join(cp_dir, prefix + "????????") + cp_list = glob.glob(pattern) + if len(cp_list) == 0: + return None + return sorted(cp_list)[-1] diff --git a/third_party/Matcha-TTS/matcha/models/__init__.py b/third_party/Matcha-TTS/matcha/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/Matcha-TTS/matcha/models/baselightningmodule.py b/third_party/Matcha-TTS/matcha/models/baselightningmodule.py new file mode 100644 index 0000000000000000000000000000000000000000..3724888090e36b5f55445d33a87fcdae687b35a5 --- /dev/null +++ b/third_party/Matcha-TTS/matcha/models/baselightningmodule.py @@ -0,0 +1,209 @@ +""" +This is a base lightning module that can be used to train a model. +The benefit of this abstraction is that all the logic outside of model definition can be reused for different models. +""" +import inspect +from abc import ABC +from typing import Any, Dict + +import torch +from lightning import LightningModule +from lightning.pytorch.utilities import grad_norm + +from matcha import utils +from matcha.utils.utils import plot_tensor + +log = utils.get_pylogger(__name__) + + +class BaseLightningClass(LightningModule, ABC): + def update_data_statistics(self, data_statistics): + if data_statistics is None: + data_statistics = { + "mel_mean": 0.0, + "mel_std": 1.0, + } + + self.register_buffer("mel_mean", torch.tensor(data_statistics["mel_mean"])) + self.register_buffer("mel_std", torch.tensor(data_statistics["mel_std"])) + + def configure_optimizers(self) -> Any: + optimizer = self.hparams.optimizer(params=self.parameters()) + if self.hparams.scheduler not in (None, {}): + scheduler_args = {} + # Manage last epoch for exponential schedulers + if "last_epoch" in inspect.signature(self.hparams.scheduler.scheduler).parameters: + if hasattr(self, "ckpt_loaded_epoch"): + current_epoch = self.ckpt_loaded_epoch - 1 + else: + current_epoch = -1 + + scheduler_args.update({"optimizer": optimizer}) + scheduler = self.hparams.scheduler.scheduler(**scheduler_args) + scheduler.last_epoch = current_epoch + return { + "optimizer": optimizer, + "lr_scheduler": { + "scheduler": scheduler, + "interval": self.hparams.scheduler.lightning_args.interval, + "frequency": self.hparams.scheduler.lightning_args.frequency, + "name": "learning_rate", + }, + } + + return {"optimizer": optimizer} + + def get_losses(self, batch): + x, x_lengths = batch["x"], batch["x_lengths"] + y, y_lengths = batch["y"], batch["y_lengths"] + spks = batch["spks"] + + dur_loss, prior_loss, diff_loss = self( + x=x, + x_lengths=x_lengths, + y=y, + y_lengths=y_lengths, + spks=spks, + out_size=self.out_size, + ) + return { + "dur_loss": dur_loss, + "prior_loss": prior_loss, + "diff_loss": diff_loss, + } + + def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None: + self.ckpt_loaded_epoch = checkpoint["epoch"] # pylint: disable=attribute-defined-outside-init + + def training_step(self, batch: Any, batch_idx: int): + loss_dict = self.get_losses(batch) + self.log( + "step", + float(self.global_step), + on_step=True, + prog_bar=True, + logger=True, + sync_dist=True, + ) + + self.log( + "sub_loss/train_dur_loss", + loss_dict["dur_loss"], + on_step=True, + on_epoch=True, + logger=True, + sync_dist=True, + ) + self.log( + "sub_loss/train_prior_loss", + loss_dict["prior_loss"], + on_step=True, + on_epoch=True, + logger=True, + sync_dist=True, + ) + self.log( + "sub_loss/train_diff_loss", + loss_dict["diff_loss"], + on_step=True, + on_epoch=True, + logger=True, + sync_dist=True, + ) + + total_loss = sum(loss_dict.values()) + self.log( + "loss/train", + total_loss, + on_step=True, + on_epoch=True, + logger=True, + prog_bar=True, + sync_dist=True, + ) + + return {"loss": total_loss, "log": loss_dict} + + def validation_step(self, batch: Any, batch_idx: int): + loss_dict = self.get_losses(batch) + self.log( + "sub_loss/val_dur_loss", + loss_dict["dur_loss"], + on_step=True, + on_epoch=True, + logger=True, + sync_dist=True, + ) + self.log( + "sub_loss/val_prior_loss", + loss_dict["prior_loss"], + on_step=True, + on_epoch=True, + logger=True, + sync_dist=True, + ) + self.log( + "sub_loss/val_diff_loss", + loss_dict["diff_loss"], + on_step=True, + on_epoch=True, + logger=True, + sync_dist=True, + ) + + total_loss = sum(loss_dict.values()) + self.log( + "loss/val", + total_loss, + on_step=True, + on_epoch=True, + logger=True, + prog_bar=True, + sync_dist=True, + ) + + return total_loss + + def on_validation_end(self) -> None: + if self.trainer.is_global_zero: + one_batch = next(iter(self.trainer.val_dataloaders)) + if self.current_epoch == 0: + log.debug("Plotting original samples") + for i in range(2): + y = one_batch["y"][i].unsqueeze(0).to(self.device) + self.logger.experiment.add_image( + f"original/{i}", + plot_tensor(y.squeeze().cpu()), + self.current_epoch, + dataformats="HWC", + ) + + log.debug("Synthesising...") + for i in range(2): + x = one_batch["x"][i].unsqueeze(0).to(self.device) + x_lengths = one_batch["x_lengths"][i].unsqueeze(0).to(self.device) + spks = one_batch["spks"][i].unsqueeze(0).to(self.device) if one_batch["spks"] is not None else None + output = self.synthesise(x[:, :x_lengths], x_lengths, n_timesteps=10, spks=spks) + y_enc, y_dec = output["encoder_outputs"], output["decoder_outputs"] + attn = output["attn"] + self.logger.experiment.add_image( + f"generated_enc/{i}", + plot_tensor(y_enc.squeeze().cpu()), + self.current_epoch, + dataformats="HWC", + ) + self.logger.experiment.add_image( + f"generated_dec/{i}", + plot_tensor(y_dec.squeeze().cpu()), + self.current_epoch, + dataformats="HWC", + ) + self.logger.experiment.add_image( + f"alignment/{i}", + plot_tensor(attn.squeeze().cpu()), + self.current_epoch, + dataformats="HWC", + ) + + def on_before_optimizer_step(self, optimizer): + self.log_dict({f"grad_norm/{k}": v for k, v in grad_norm(self, norm_type=2).items()}) diff --git a/third_party/Matcha-TTS/matcha/models/components/__init__.py b/third_party/Matcha-TTS/matcha/models/components/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/Matcha-TTS/matcha/models/components/decoder.py b/third_party/Matcha-TTS/matcha/models/components/decoder.py new file mode 100644 index 0000000000000000000000000000000000000000..1137cd7008e9d07b4f306926a82e44c2b2cddbdf --- /dev/null +++ b/third_party/Matcha-TTS/matcha/models/components/decoder.py @@ -0,0 +1,443 @@ +import math +from typing import Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +from conformer import ConformerBlock +from diffusers.models.activations import get_activation +from einops import pack, rearrange, repeat + +from matcha.models.components.transformer import BasicTransformerBlock + + +class SinusoidalPosEmb(torch.nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + assert self.dim % 2 == 0, "SinusoidalPosEmb requires dim to be even" + + def forward(self, x, scale=1000): + if x.ndim < 1: + x = x.unsqueeze(0) + device = x.device + half_dim = self.dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb) + emb = scale * x.unsqueeze(1) * emb.unsqueeze(0) + emb = torch.cat((emb.sin(), emb.cos()), dim=-1) + return emb + + +class Block1D(torch.nn.Module): + def __init__(self, dim, dim_out, groups=8): + super().__init__() + self.block = torch.nn.Sequential( + torch.nn.Conv1d(dim, dim_out, 3, padding=1), + torch.nn.GroupNorm(groups, dim_out), + nn.Mish(), + ) + + def forward(self, x, mask): + output = self.block(x * mask) + return output * mask + + +class ResnetBlock1D(torch.nn.Module): + def __init__(self, dim, dim_out, time_emb_dim, groups=8): + super().__init__() + self.mlp = torch.nn.Sequential(nn.Mish(), torch.nn.Linear(time_emb_dim, dim_out)) + + self.block1 = Block1D(dim, dim_out, groups=groups) + self.block2 = Block1D(dim_out, dim_out, groups=groups) + + self.res_conv = torch.nn.Conv1d(dim, dim_out, 1) + + def forward(self, x, mask, time_emb): + h = self.block1(x, mask) + h += self.mlp(time_emb).unsqueeze(-1) + h = self.block2(h, mask) + output = h + self.res_conv(x * mask) + return output + + +class Downsample1D(nn.Module): + def __init__(self, dim): + super().__init__() + self.conv = torch.nn.Conv1d(dim, dim, 3, 2, 1) + + def forward(self, x): + return self.conv(x) + + +class TimestepEmbedding(nn.Module): + def __init__( + self, + in_channels: int, + time_embed_dim: int, + act_fn: str = "silu", + out_dim: int = None, + post_act_fn: Optional[str] = None, + cond_proj_dim=None, + ): + super().__init__() + + self.linear_1 = nn.Linear(in_channels, time_embed_dim) + + if cond_proj_dim is not None: + self.cond_proj = nn.Linear(cond_proj_dim, in_channels, bias=False) + else: + self.cond_proj = None + + self.act = get_activation(act_fn) + + if out_dim is not None: + time_embed_dim_out = out_dim + else: + time_embed_dim_out = time_embed_dim + self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim_out) + + if post_act_fn is None: + self.post_act = None + else: + self.post_act = get_activation(post_act_fn) + + def forward(self, sample, condition=None): + if condition is not None: + sample = sample + self.cond_proj(condition) + sample = self.linear_1(sample) + + if self.act is not None: + sample = self.act(sample) + + sample = self.linear_2(sample) + + if self.post_act is not None: + sample = self.post_act(sample) + return sample + + +class Upsample1D(nn.Module): + """A 1D upsampling layer with an optional convolution. + + Parameters: + channels (`int`): + number of channels in the inputs and outputs. + use_conv (`bool`, default `False`): + option to use a convolution. + use_conv_transpose (`bool`, default `False`): + option to use a convolution transpose. + out_channels (`int`, optional): + number of output channels. Defaults to `channels`. + """ + + def __init__(self, channels, use_conv=False, use_conv_transpose=True, out_channels=None, name="conv"): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_conv_transpose = use_conv_transpose + self.name = name + + self.conv = None + if use_conv_transpose: + self.conv = nn.ConvTranspose1d(channels, self.out_channels, 4, 2, 1) + elif use_conv: + self.conv = nn.Conv1d(self.channels, self.out_channels, 3, padding=1) + + def forward(self, inputs): + assert inputs.shape[1] == self.channels + if self.use_conv_transpose: + return self.conv(inputs) + + outputs = F.interpolate(inputs, scale_factor=2.0, mode="nearest") + + if self.use_conv: + outputs = self.conv(outputs) + + return outputs + + +class ConformerWrapper(ConformerBlock): + def __init__( # pylint: disable=useless-super-delegation + self, + *, + dim, + dim_head=64, + heads=8, + ff_mult=4, + conv_expansion_factor=2, + conv_kernel_size=31, + attn_dropout=0, + ff_dropout=0, + conv_dropout=0, + conv_causal=False, + ): + super().__init__( + dim=dim, + dim_head=dim_head, + heads=heads, + ff_mult=ff_mult, + conv_expansion_factor=conv_expansion_factor, + conv_kernel_size=conv_kernel_size, + attn_dropout=attn_dropout, + ff_dropout=ff_dropout, + conv_dropout=conv_dropout, + conv_causal=conv_causal, + ) + + def forward( + self, + hidden_states, + attention_mask, + encoder_hidden_states=None, + encoder_attention_mask=None, + timestep=None, + ): + return super().forward(x=hidden_states, mask=attention_mask.bool()) + + +class Decoder(nn.Module): + def __init__( + self, + in_channels, + out_channels, + channels=(256, 256), + dropout=0.05, + attention_head_dim=64, + n_blocks=1, + num_mid_blocks=2, + num_heads=4, + act_fn="snake", + down_block_type="transformer", + mid_block_type="transformer", + up_block_type="transformer", + ): + super().__init__() + channels = tuple(channels) + self.in_channels = in_channels + self.out_channels = out_channels + + self.time_embeddings = SinusoidalPosEmb(in_channels) + time_embed_dim = channels[0] * 4 + self.time_mlp = TimestepEmbedding( + in_channels=in_channels, + time_embed_dim=time_embed_dim, + act_fn="silu", + ) + + self.down_blocks = nn.ModuleList([]) + self.mid_blocks = nn.ModuleList([]) + self.up_blocks = nn.ModuleList([]) + + output_channel = in_channels + for i in range(len(channels)): # pylint: disable=consider-using-enumerate + input_channel = output_channel + output_channel = channels[i] + is_last = i == len(channels) - 1 + resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) + transformer_blocks = nn.ModuleList( + [ + self.get_block( + down_block_type, + output_channel, + attention_head_dim, + num_heads, + dropout, + act_fn, + ) + for _ in range(n_blocks) + ] + ) + downsample = ( + Downsample1D(output_channel) if not is_last else nn.Conv1d(output_channel, output_channel, 3, padding=1) + ) + + self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample])) + + for i in range(num_mid_blocks): + input_channel = channels[-1] + out_channels = channels[-1] + + resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) + + transformer_blocks = nn.ModuleList( + [ + self.get_block( + mid_block_type, + output_channel, + attention_head_dim, + num_heads, + dropout, + act_fn, + ) + for _ in range(n_blocks) + ] + ) + + self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks])) + + channels = channels[::-1] + (channels[0],) + for i in range(len(channels) - 1): + input_channel = channels[i] + output_channel = channels[i + 1] + is_last = i == len(channels) - 2 + + resnet = ResnetBlock1D( + dim=2 * input_channel, + dim_out=output_channel, + time_emb_dim=time_embed_dim, + ) + transformer_blocks = nn.ModuleList( + [ + self.get_block( + up_block_type, + output_channel, + attention_head_dim, + num_heads, + dropout, + act_fn, + ) + for _ in range(n_blocks) + ] + ) + upsample = ( + Upsample1D(output_channel, use_conv_transpose=True) + if not is_last + else nn.Conv1d(output_channel, output_channel, 3, padding=1) + ) + + self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample])) + + self.final_block = Block1D(channels[-1], channels[-1]) + self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1) + + self.initialize_weights() + # nn.init.normal_(self.final_proj.weight) + + @staticmethod + def get_block(block_type, dim, attention_head_dim, num_heads, dropout, act_fn): + if block_type == "conformer": + block = ConformerWrapper( + dim=dim, + dim_head=attention_head_dim, + heads=num_heads, + ff_mult=1, + conv_expansion_factor=2, + ff_dropout=dropout, + attn_dropout=dropout, + conv_dropout=dropout, + conv_kernel_size=31, + ) + elif block_type == "transformer": + block = BasicTransformerBlock( + dim=dim, + num_attention_heads=num_heads, + attention_head_dim=attention_head_dim, + dropout=dropout, + activation_fn=act_fn, + ) + else: + raise ValueError(f"Unknown block type {block_type}") + + return block + + def initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv1d): + nn.init.kaiming_normal_(m.weight, nonlinearity="relu") + + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + elif isinstance(m, nn.GroupNorm): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + elif isinstance(m, nn.Linear): + nn.init.kaiming_normal_(m.weight, nonlinearity="relu") + + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + def forward(self, x, mask, mu, t, spks=None, cond=None): + """Forward pass of the UNet1DConditional model. + + Args: + x (torch.Tensor): shape (batch_size, in_channels, time) + mask (_type_): shape (batch_size, 1, time) + t (_type_): shape (batch_size) + spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None. + cond (_type_, optional): placeholder for future use. Defaults to None. + + Raises: + ValueError: _description_ + ValueError: _description_ + + Returns: + _type_: _description_ + """ + + t = self.time_embeddings(t) + t = self.time_mlp(t) + + x = pack([x, mu], "b * t")[0] + + if spks is not None: + spks = repeat(spks, "b c -> b c t", t=x.shape[-1]) + x = pack([x, spks], "b * t")[0] + + hiddens = [] + masks = [mask] + for resnet, transformer_blocks, downsample in self.down_blocks: + mask_down = masks[-1] + x = resnet(x, mask_down, t) + x = rearrange(x, "b c t -> b t c") + mask_down = rearrange(mask_down, "b 1 t -> b t") + for transformer_block in transformer_blocks: + x = transformer_block( + hidden_states=x, + attention_mask=mask_down, + timestep=t, + ) + x = rearrange(x, "b t c -> b c t") + mask_down = rearrange(mask_down, "b t -> b 1 t") + hiddens.append(x) # Save hidden states for skip connections + x = downsample(x * mask_down) + masks.append(mask_down[:, :, ::2]) + + masks = masks[:-1] + mask_mid = masks[-1] + + for resnet, transformer_blocks in self.mid_blocks: + x = resnet(x, mask_mid, t) + x = rearrange(x, "b c t -> b t c") + mask_mid = rearrange(mask_mid, "b 1 t -> b t") + for transformer_block in transformer_blocks: + x = transformer_block( + hidden_states=x, + attention_mask=mask_mid, + timestep=t, + ) + x = rearrange(x, "b t c -> b c t") + mask_mid = rearrange(mask_mid, "b t -> b 1 t") + + for resnet, transformer_blocks, upsample in self.up_blocks: + mask_up = masks.pop() + x = resnet(pack([x, hiddens.pop()], "b * t")[0], mask_up, t) + x = rearrange(x, "b c t -> b t c") + mask_up = rearrange(mask_up, "b 1 t -> b t") + for transformer_block in transformer_blocks: + x = transformer_block( + hidden_states=x, + attention_mask=mask_up, + timestep=t, + ) + x = rearrange(x, "b t c -> b c t") + mask_up = rearrange(mask_up, "b t -> b 1 t") + x = upsample(x * mask_up) + + x = self.final_block(x, mask_up) + output = self.final_proj(x * mask_up) + + return output * mask diff --git a/third_party/Matcha-TTS/matcha/models/components/flow_matching.py b/third_party/Matcha-TTS/matcha/models/components/flow_matching.py new file mode 100644 index 0000000000000000000000000000000000000000..5cad7431ef66a8d11da32a77c1af7f6e31d6b774 --- /dev/null +++ b/third_party/Matcha-TTS/matcha/models/components/flow_matching.py @@ -0,0 +1,132 @@ +from abc import ABC + +import torch +import torch.nn.functional as F + +from matcha.models.components.decoder import Decoder +from matcha.utils.pylogger import get_pylogger + +log = get_pylogger(__name__) + + +class BASECFM(torch.nn.Module, ABC): + def __init__( + self, + n_feats, + cfm_params, + n_spks=1, + spk_emb_dim=128, + ): + super().__init__() + self.n_feats = n_feats + self.n_spks = n_spks + self.spk_emb_dim = spk_emb_dim + self.solver = cfm_params.solver + if hasattr(cfm_params, "sigma_min"): + self.sigma_min = cfm_params.sigma_min + else: + self.sigma_min = 1e-4 + + self.estimator = None + + @torch.inference_mode() + def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None): + """Forward diffusion + + Args: + mu (torch.Tensor): output of encoder + shape: (batch_size, n_feats, mel_timesteps) + mask (torch.Tensor): output_mask + shape: (batch_size, 1, mel_timesteps) + n_timesteps (int): number of diffusion steps + temperature (float, optional): temperature for scaling noise. Defaults to 1.0. + spks (torch.Tensor, optional): speaker ids. Defaults to None. + shape: (batch_size, spk_emb_dim) + cond: Not used but kept for future purposes + + Returns: + sample: generated mel-spectrogram + shape: (batch_size, n_feats, mel_timesteps) + """ + z = torch.randn_like(mu) * temperature + t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device) + return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond) + + def solve_euler(self, x, t_span, mu, mask, spks, cond): + """ + Fixed euler solver for ODEs. + Args: + x (torch.Tensor): random noise + t_span (torch.Tensor): n_timesteps interpolated + shape: (n_timesteps + 1,) + mu (torch.Tensor): output of encoder + shape: (batch_size, n_feats, mel_timesteps) + mask (torch.Tensor): output_mask + shape: (batch_size, 1, mel_timesteps) + spks (torch.Tensor, optional): speaker ids. Defaults to None. + shape: (batch_size, spk_emb_dim) + cond: Not used but kept for future purposes + """ + t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0] + + # I am storing this because I can later plot it by putting a debugger here and saving it to a file + # Or in future might add like a return_all_steps flag + sol = [] + + for step in range(1, len(t_span)): + dphi_dt = self.estimator(x, mask, mu, t, spks, cond) + + x = x + dt * dphi_dt + t = t + dt + sol.append(x) + if step < len(t_span) - 1: + dt = t_span[step + 1] - t + + return sol[-1] + + def compute_loss(self, x1, mask, mu, spks=None, cond=None): + """Computes diffusion loss + + Args: + x1 (torch.Tensor): Target + shape: (batch_size, n_feats, mel_timesteps) + mask (torch.Tensor): target mask + shape: (batch_size, 1, mel_timesteps) + mu (torch.Tensor): output of encoder + shape: (batch_size, n_feats, mel_timesteps) + spks (torch.Tensor, optional): speaker embedding. Defaults to None. + shape: (batch_size, spk_emb_dim) + + Returns: + loss: conditional flow matching loss + y: conditional flow + shape: (batch_size, n_feats, mel_timesteps) + """ + b, _, t = mu.shape + + # random timestep + t = torch.rand([b, 1, 1], device=mu.device, dtype=mu.dtype) + # sample noise p(x_0) + z = torch.randn_like(x1) + + y = (1 - (1 - self.sigma_min) * t) * z + t * x1 + u = x1 - (1 - self.sigma_min) * z + + loss = F.mse_loss(self.estimator(y, mask, mu, t.squeeze(), spks), u, reduction="sum") / ( + torch.sum(mask) * u.shape[1] + ) + return loss, y + + +class CFM(BASECFM): + def __init__(self, in_channels, out_channel, cfm_params, decoder_params, n_spks=1, spk_emb_dim=64): + super().__init__( + n_feats=in_channels, + cfm_params=cfm_params, + n_spks=n_spks, + spk_emb_dim=spk_emb_dim, + ) + + in_channels = in_channels + (spk_emb_dim if n_spks > 1 else 0) + # Just change the architecture of the estimator here + self.estimator = Decoder(in_channels=in_channels, out_channels=out_channel, **decoder_params) diff --git a/third_party/Matcha-TTS/matcha/models/components/text_encoder.py b/third_party/Matcha-TTS/matcha/models/components/text_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..a388d05d6351fa2c9d9632fed0942d51fbec067b --- /dev/null +++ b/third_party/Matcha-TTS/matcha/models/components/text_encoder.py @@ -0,0 +1,410 @@ +""" from https://github.com/jaywalnut310/glow-tts """ + +import math + +import torch +import torch.nn as nn +from einops import rearrange + +import matcha.utils as utils +from matcha.utils.model import sequence_mask + +log = utils.get_pylogger(__name__) + + +class LayerNorm(nn.Module): + def __init__(self, channels, eps=1e-4): + super().__init__() + self.channels = channels + self.eps = eps + + self.gamma = torch.nn.Parameter(torch.ones(channels)) + self.beta = torch.nn.Parameter(torch.zeros(channels)) + + def forward(self, x): + n_dims = len(x.shape) + mean = torch.mean(x, 1, keepdim=True) + variance = torch.mean((x - mean) ** 2, 1, keepdim=True) + + x = (x - mean) * torch.rsqrt(variance + self.eps) + + shape = [1, -1] + [1] * (n_dims - 2) + x = x * self.gamma.view(*shape) + self.beta.view(*shape) + return x + + +class ConvReluNorm(nn.Module): + def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): + super().__init__() + self.in_channels = in_channels + self.hidden_channels = hidden_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.n_layers = n_layers + self.p_dropout = p_dropout + + self.conv_layers = torch.nn.ModuleList() + self.norm_layers = torch.nn.ModuleList() + self.conv_layers.append(torch.nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) + self.norm_layers.append(LayerNorm(hidden_channels)) + self.relu_drop = torch.nn.Sequential(torch.nn.ReLU(), torch.nn.Dropout(p_dropout)) + for _ in range(n_layers - 1): + self.conv_layers.append( + torch.nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2) + ) + self.norm_layers.append(LayerNorm(hidden_channels)) + self.proj = torch.nn.Conv1d(hidden_channels, out_channels, 1) + self.proj.weight.data.zero_() + self.proj.bias.data.zero_() + + def forward(self, x, x_mask): + x_org = x + for i in range(self.n_layers): + x = self.conv_layers[i](x * x_mask) + x = self.norm_layers[i](x) + x = self.relu_drop(x) + x = x_org + self.proj(x) + return x * x_mask + + +class DurationPredictor(nn.Module): + def __init__(self, in_channels, filter_channels, kernel_size, p_dropout): + super().__init__() + self.in_channels = in_channels + self.filter_channels = filter_channels + self.p_dropout = p_dropout + + self.drop = torch.nn.Dropout(p_dropout) + self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2) + self.norm_1 = LayerNorm(filter_channels) + self.conv_2 = torch.nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2) + self.norm_2 = LayerNorm(filter_channels) + self.proj = torch.nn.Conv1d(filter_channels, 1, 1) + + def forward(self, x, x_mask): + x = self.conv_1(x * x_mask) + x = torch.relu(x) + x = self.norm_1(x) + x = self.drop(x) + x = self.conv_2(x * x_mask) + x = torch.relu(x) + x = self.norm_2(x) + x = self.drop(x) + x = self.proj(x * x_mask) + return x * x_mask + + +class RotaryPositionalEmbeddings(nn.Module): + """ + ## RoPE module + + Rotary encoding transforms pairs of features by rotating in the 2D plane. + That is, it organizes the $d$ features as $\frac{d}{2}$ pairs. + Each pair can be considered a coordinate in a 2D plane, and the encoding will rotate it + by an angle depending on the position of the token. + """ + + def __init__(self, d: int, base: int = 10_000): + r""" + * `d` is the number of features $d$ + * `base` is the constant used for calculating $\Theta$ + """ + super().__init__() + + self.base = base + self.d = int(d) + self.cos_cached = None + self.sin_cached = None + + def _build_cache(self, x: torch.Tensor): + r""" + Cache $\cos$ and $\sin$ values + """ + # Return if cache is already built + if self.cos_cached is not None and x.shape[0] <= self.cos_cached.shape[0]: + return + + # Get sequence length + seq_len = x.shape[0] + + # $\Theta = {\theta_i = 10000^{-\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$ + theta = 1.0 / (self.base ** (torch.arange(0, self.d, 2).float() / self.d)).to(x.device) + + # Create position indexes `[0, 1, ..., seq_len - 1]` + seq_idx = torch.arange(seq_len, device=x.device).float().to(x.device) + + # Calculate the product of position index and $\theta_i$ + idx_theta = torch.einsum("n,d->nd", seq_idx, theta) + + # Concatenate so that for row $m$ we have + # $[m \theta_0, m \theta_1, ..., m \theta_{\frac{d}{2}}, m \theta_0, m \theta_1, ..., m \theta_{\frac{d}{2}}]$ + idx_theta2 = torch.cat([idx_theta, idx_theta], dim=1) + + # Cache them + self.cos_cached = idx_theta2.cos()[:, None, None, :] + self.sin_cached = idx_theta2.sin()[:, None, None, :] + + def _neg_half(self, x: torch.Tensor): + # $\frac{d}{2}$ + d_2 = self.d // 2 + + # Calculate $[-x^{(\frac{d}{2} + 1)}, -x^{(\frac{d}{2} + 2)}, ..., -x^{(d)}, x^{(1)}, x^{(2)}, ..., x^{(\frac{d}{2})}]$ + return torch.cat([-x[:, :, :, d_2:], x[:, :, :, :d_2]], dim=-1) + + def forward(self, x: torch.Tensor): + """ + * `x` is the Tensor at the head of a key or a query with shape `[seq_len, batch_size, n_heads, d]` + """ + # Cache $\cos$ and $\sin$ values + x = rearrange(x, "b h t d -> t b h d") + + self._build_cache(x) + + # Split the features, we can choose to apply rotary embeddings only to a partial set of features. + x_rope, x_pass = x[..., : self.d], x[..., self.d :] + + # Calculate + # $[-x^{(\frac{d}{2} + 1)}, -x^{(\frac{d}{2} + 2)}, ..., -x^{(d)}, x^{(1)}, x^{(2)}, ..., x^{(\frac{d}{2})}]$ + neg_half_x = self._neg_half(x_rope) + + x_rope = (x_rope * self.cos_cached[: x.shape[0]]) + (neg_half_x * self.sin_cached[: x.shape[0]]) + + return rearrange(torch.cat((x_rope, x_pass), dim=-1), "t b h d -> b h t d") + + +class MultiHeadAttention(nn.Module): + def __init__( + self, + channels, + out_channels, + n_heads, + heads_share=True, + p_dropout=0.0, + proximal_bias=False, + proximal_init=False, + ): + super().__init__() + assert channels % n_heads == 0 + + self.channels = channels + self.out_channels = out_channels + self.n_heads = n_heads + self.heads_share = heads_share + self.proximal_bias = proximal_bias + self.p_dropout = p_dropout + self.attn = None + + self.k_channels = channels // n_heads + self.conv_q = torch.nn.Conv1d(channels, channels, 1) + self.conv_k = torch.nn.Conv1d(channels, channels, 1) + self.conv_v = torch.nn.Conv1d(channels, channels, 1) + + # from https://nn.labml.ai/transformers/rope/index.html + self.query_rotary_pe = RotaryPositionalEmbeddings(self.k_channels * 0.5) + self.key_rotary_pe = RotaryPositionalEmbeddings(self.k_channels * 0.5) + + self.conv_o = torch.nn.Conv1d(channels, out_channels, 1) + self.drop = torch.nn.Dropout(p_dropout) + + torch.nn.init.xavier_uniform_(self.conv_q.weight) + torch.nn.init.xavier_uniform_(self.conv_k.weight) + if proximal_init: + self.conv_k.weight.data.copy_(self.conv_q.weight.data) + self.conv_k.bias.data.copy_(self.conv_q.bias.data) + torch.nn.init.xavier_uniform_(self.conv_v.weight) + + def forward(self, x, c, attn_mask=None): + q = self.conv_q(x) + k = self.conv_k(c) + v = self.conv_v(c) + + x, self.attn = self.attention(q, k, v, mask=attn_mask) + + x = self.conv_o(x) + return x + + def attention(self, query, key, value, mask=None): + b, d, t_s, t_t = (*key.size(), query.size(2)) + query = rearrange(query, "b (h c) t-> b h t c", h=self.n_heads) + key = rearrange(key, "b (h c) t-> b h t c", h=self.n_heads) + value = rearrange(value, "b (h c) t-> b h t c", h=self.n_heads) + + query = self.query_rotary_pe(query) + key = self.key_rotary_pe(key) + + scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels) + + if self.proximal_bias: + assert t_s == t_t, "Proximal bias is only available for self-attention." + scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype) + if mask is not None: + scores = scores.masked_fill(mask == 0, -1e4) + p_attn = torch.nn.functional.softmax(scores, dim=-1) + p_attn = self.drop(p_attn) + output = torch.matmul(p_attn, value) + output = output.transpose(2, 3).contiguous().view(b, d, t_t) + return output, p_attn + + @staticmethod + def _attention_bias_proximal(length): + r = torch.arange(length, dtype=torch.float32) + diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) + return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) + + +class FFN(nn.Module): + def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0.0): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.filter_channels = filter_channels + self.kernel_size = kernel_size + self.p_dropout = p_dropout + + self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2) + self.conv_2 = torch.nn.Conv1d(filter_channels, out_channels, kernel_size, padding=kernel_size // 2) + self.drop = torch.nn.Dropout(p_dropout) + + def forward(self, x, x_mask): + x = self.conv_1(x * x_mask) + x = torch.relu(x) + x = self.drop(x) + x = self.conv_2(x * x_mask) + return x * x_mask + + +class Encoder(nn.Module): + def __init__( + self, + hidden_channels, + filter_channels, + n_heads, + n_layers, + kernel_size=1, + p_dropout=0.0, + **kwargs, + ): + super().__init__() + self.hidden_channels = hidden_channels + self.filter_channels = filter_channels + self.n_heads = n_heads + self.n_layers = n_layers + self.kernel_size = kernel_size + self.p_dropout = p_dropout + + self.drop = torch.nn.Dropout(p_dropout) + self.attn_layers = torch.nn.ModuleList() + self.norm_layers_1 = torch.nn.ModuleList() + self.ffn_layers = torch.nn.ModuleList() + self.norm_layers_2 = torch.nn.ModuleList() + for _ in range(self.n_layers): + self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout)) + self.norm_layers_1.append(LayerNorm(hidden_channels)) + self.ffn_layers.append( + FFN( + hidden_channels, + hidden_channels, + filter_channels, + kernel_size, + p_dropout=p_dropout, + ) + ) + self.norm_layers_2.append(LayerNorm(hidden_channels)) + + def forward(self, x, x_mask): + attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) + for i in range(self.n_layers): + x = x * x_mask + y = self.attn_layers[i](x, x, attn_mask) + y = self.drop(y) + x = self.norm_layers_1[i](x + y) + y = self.ffn_layers[i](x, x_mask) + y = self.drop(y) + x = self.norm_layers_2[i](x + y) + x = x * x_mask + return x + + +class TextEncoder(nn.Module): + def __init__( + self, + encoder_type, + encoder_params, + duration_predictor_params, + n_vocab, + n_spks=1, + spk_emb_dim=128, + ): + super().__init__() + self.encoder_type = encoder_type + self.n_vocab = n_vocab + self.n_feats = encoder_params.n_feats + self.n_channels = encoder_params.n_channels + self.spk_emb_dim = spk_emb_dim + self.n_spks = n_spks + + self.emb = torch.nn.Embedding(n_vocab, self.n_channels) + torch.nn.init.normal_(self.emb.weight, 0.0, self.n_channels**-0.5) + + if encoder_params.prenet: + self.prenet = ConvReluNorm( + self.n_channels, + self.n_channels, + self.n_channels, + kernel_size=5, + n_layers=3, + p_dropout=0.5, + ) + else: + self.prenet = lambda x, x_mask: x + + self.encoder = Encoder( + encoder_params.n_channels + (spk_emb_dim if n_spks > 1 else 0), + encoder_params.filter_channels, + encoder_params.n_heads, + encoder_params.n_layers, + encoder_params.kernel_size, + encoder_params.p_dropout, + ) + + self.proj_m = torch.nn.Conv1d(self.n_channels + (spk_emb_dim if n_spks > 1 else 0), self.n_feats, 1) + self.proj_w = DurationPredictor( + self.n_channels + (spk_emb_dim if n_spks > 1 else 0), + duration_predictor_params.filter_channels_dp, + duration_predictor_params.kernel_size, + duration_predictor_params.p_dropout, + ) + + def forward(self, x, x_lengths, spks=None): + """Run forward pass to the transformer based encoder and duration predictor + + Args: + x (torch.Tensor): text input + shape: (batch_size, max_text_length) + x_lengths (torch.Tensor): text input lengths + shape: (batch_size,) + spks (torch.Tensor, optional): speaker ids. Defaults to None. + shape: (batch_size,) + + Returns: + mu (torch.Tensor): average output of the encoder + shape: (batch_size, n_feats, max_text_length) + logw (torch.Tensor): log duration predicted by the duration predictor + shape: (batch_size, 1, max_text_length) + x_mask (torch.Tensor): mask for the text input + shape: (batch_size, 1, max_text_length) + """ + x = self.emb(x) * math.sqrt(self.n_channels) + x = torch.transpose(x, 1, -1) + x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) + + x = self.prenet(x, x_mask) + if self.n_spks > 1: + x = torch.cat([x, spks.unsqueeze(-1).repeat(1, 1, x.shape[-1])], dim=1) + x = self.encoder(x, x_mask) + mu = self.proj_m(x) * x_mask + + x_dp = torch.detach(x) + logw = self.proj_w(x_dp, x_mask) + + return mu, logw, x_mask diff --git a/third_party/Matcha-TTS/matcha/models/components/transformer.py b/third_party/Matcha-TTS/matcha/models/components/transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..dd1afa3aff5383912209e508676c6885e13ef4ee --- /dev/null +++ b/third_party/Matcha-TTS/matcha/models/components/transformer.py @@ -0,0 +1,316 @@ +from typing import Any, Dict, Optional + +import torch +import torch.nn as nn +from diffusers.models.attention import ( + GEGLU, + GELU, + AdaLayerNorm, + AdaLayerNormZero, + ApproximateGELU, +) +from diffusers.models.attention_processor import Attention +from diffusers.models.lora import LoRACompatibleLinear +from diffusers.utils.torch_utils import maybe_allow_in_graph + + +class SnakeBeta(nn.Module): + """ + A modified Snake function which uses separate parameters for the magnitude of the periodic components + Shape: + - Input: (B, C, T) + - Output: (B, C, T), same shape as the input + Parameters: + - alpha - trainable parameter that controls frequency + - beta - trainable parameter that controls magnitude + References: + - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: + https://arxiv.org/abs/2006.08195 + Examples: + >>> a1 = snakebeta(256) + >>> x = torch.randn(256) + >>> x = a1(x) + """ + + def __init__(self, in_features, out_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True): + """ + Initialization. + INPUT: + - in_features: shape of the input + - alpha - trainable parameter that controls frequency + - beta - trainable parameter that controls magnitude + alpha is initialized to 1 by default, higher values = higher-frequency. + beta is initialized to 1 by default, higher values = higher-magnitude. + alpha will be trained along with the rest of your model. + """ + super().__init__() + self.in_features = out_features if isinstance(out_features, list) else [out_features] + self.proj = LoRACompatibleLinear(in_features, out_features) + + # initialize alpha + self.alpha_logscale = alpha_logscale + if self.alpha_logscale: # log scale alphas initialized to zeros + self.alpha = nn.Parameter(torch.zeros(self.in_features) * alpha) + self.beta = nn.Parameter(torch.zeros(self.in_features) * alpha) + else: # linear scale alphas initialized to ones + self.alpha = nn.Parameter(torch.ones(self.in_features) * alpha) + self.beta = nn.Parameter(torch.ones(self.in_features) * alpha) + + self.alpha.requires_grad = alpha_trainable + self.beta.requires_grad = alpha_trainable + + self.no_div_by_zero = 0.000000001 + + def forward(self, x): + """ + Forward pass of the function. + Applies the function to the input elementwise. + SnakeBeta ∶= x + 1/b * sin^2 (xa) + """ + x = self.proj(x) + if self.alpha_logscale: + alpha = torch.exp(self.alpha) + beta = torch.exp(self.beta) + else: + alpha = self.alpha + beta = self.beta + + x = x + (1.0 / (beta + self.no_div_by_zero)) * torch.pow(torch.sin(x * alpha), 2) + + return x + + +class FeedForward(nn.Module): + r""" + A feed-forward layer. + + Parameters: + dim (`int`): The number of channels in the input. + dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`. + mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + final_dropout (`bool` *optional*, defaults to False): Apply a final dropout. + """ + + def __init__( + self, + dim: int, + dim_out: Optional[int] = None, + mult: int = 4, + dropout: float = 0.0, + activation_fn: str = "geglu", + final_dropout: bool = False, + ): + super().__init__() + inner_dim = int(dim * mult) + dim_out = dim_out if dim_out is not None else dim + + if activation_fn == "gelu": + act_fn = GELU(dim, inner_dim) + if activation_fn == "gelu-approximate": + act_fn = GELU(dim, inner_dim, approximate="tanh") + elif activation_fn == "geglu": + act_fn = GEGLU(dim, inner_dim) + elif activation_fn == "geglu-approximate": + act_fn = ApproximateGELU(dim, inner_dim) + elif activation_fn == "snakebeta": + act_fn = SnakeBeta(dim, inner_dim) + + self.net = nn.ModuleList([]) + # project in + self.net.append(act_fn) + # project dropout + self.net.append(nn.Dropout(dropout)) + # project out + self.net.append(LoRACompatibleLinear(inner_dim, dim_out)) + # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout + if final_dropout: + self.net.append(nn.Dropout(dropout)) + + def forward(self, hidden_states): + for module in self.net: + hidden_states = module(hidden_states) + return hidden_states + + +@maybe_allow_in_graph +class BasicTransformerBlock(nn.Module): + r""" + A basic Transformer block. + + Parameters: + dim (`int`): The number of channels in the input and output. + num_attention_heads (`int`): The number of heads to use for multi-head attention. + attention_head_dim (`int`): The number of channels in each head. + dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. + cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. + only_cross_attention (`bool`, *optional*): + Whether to use only cross-attention layers. In this case two cross attention layers are used. + double_self_attention (`bool`, *optional*): + Whether to use two self-attention layers. In this case no cross attention layers are used. + activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. + num_embeds_ada_norm (: + obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`. + attention_bias (: + obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. + """ + + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout=0.0, + cross_attention_dim: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + attention_bias: bool = False, + only_cross_attention: bool = False, + double_self_attention: bool = False, + upcast_attention: bool = False, + norm_elementwise_affine: bool = True, + norm_type: str = "layer_norm", + final_dropout: bool = False, + ): + super().__init__() + self.only_cross_attention = only_cross_attention + + self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero" + self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm" + + if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None: + raise ValueError( + f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to" + f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}." + ) + + # Define 3 blocks. Each block has its own normalization layer. + # 1. Self-Attn + if self.use_ada_layer_norm: + self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) + elif self.use_ada_layer_norm_zero: + self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm) + else: + self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + self.attn1 = Attention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + cross_attention_dim=cross_attention_dim if only_cross_attention else None, + upcast_attention=upcast_attention, + ) + + # 2. Cross-Attn + if cross_attention_dim is not None or double_self_attention: + # We currently only use AdaLayerNormZero for self attention where there will only be one attention block. + # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during + # the second cross attention block. + self.norm2 = ( + AdaLayerNorm(dim, num_embeds_ada_norm) + if self.use_ada_layer_norm + else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + ) + self.attn2 = Attention( + query_dim=dim, + cross_attention_dim=cross_attention_dim if not double_self_attention else None, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + # scale_qk=False, # uncomment this to not to use flash attention + ) # is self-attn if encoder_hidden_states is none + else: + self.norm2 = None + self.attn2 = None + + # 3. Feed-forward + self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine) + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout) + + # let chunk size default to None + self._chunk_size = None + self._chunk_dim = 0 + + def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int): + # Sets chunk feed-forward + self._chunk_size = chunk_size + self._chunk_dim = dim + + def forward( + self, + hidden_states: torch.FloatTensor, + attention_mask: Optional[torch.FloatTensor] = None, + encoder_hidden_states: Optional[torch.FloatTensor] = None, + encoder_attention_mask: Optional[torch.FloatTensor] = None, + timestep: Optional[torch.LongTensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, + class_labels: Optional[torch.LongTensor] = None, + ): + # Notice that normalization is always applied before the real computation in the following blocks. + # 1. Self-Attention + if self.use_ada_layer_norm: + norm_hidden_states = self.norm1(hidden_states, timestep) + elif self.use_ada_layer_norm_zero: + norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( + hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype + ) + else: + norm_hidden_states = self.norm1(hidden_states) + + cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} + + attn_output = self.attn1( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, + attention_mask=encoder_attention_mask if self.only_cross_attention else attention_mask, + **cross_attention_kwargs, + ) + if self.use_ada_layer_norm_zero: + attn_output = gate_msa.unsqueeze(1) * attn_output + hidden_states = attn_output + hidden_states + + # 2. Cross-Attention + if self.attn2 is not None: + norm_hidden_states = ( + self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states) + ) + + attn_output = self.attn2( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + **cross_attention_kwargs, + ) + hidden_states = attn_output + hidden_states + + # 3. Feed-forward + norm_hidden_states = self.norm3(hidden_states) + + if self.use_ada_layer_norm_zero: + norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + + if self._chunk_size is not None: + # "feed_forward_chunk_size" can be used to save memory + if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0: + raise ValueError( + f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`." + ) + + num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size + ff_output = torch.cat( + [self.ff(hid_slice) for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)], + dim=self._chunk_dim, + ) + else: + ff_output = self.ff(norm_hidden_states) + + if self.use_ada_layer_norm_zero: + ff_output = gate_mlp.unsqueeze(1) * ff_output + + hidden_states = ff_output + hidden_states + + return hidden_states diff --git a/third_party/Matcha-TTS/matcha/models/matcha_tts.py b/third_party/Matcha-TTS/matcha/models/matcha_tts.py new file mode 100644 index 0000000000000000000000000000000000000000..64b2c07fe8de4760aee1aed80d206112d30df55f --- /dev/null +++ b/third_party/Matcha-TTS/matcha/models/matcha_tts.py @@ -0,0 +1,239 @@ +import datetime as dt +import math +import random + +import torch + +import matcha.utils.monotonic_align as monotonic_align +from matcha import utils +from matcha.models.baselightningmodule import BaseLightningClass +from matcha.models.components.flow_matching import CFM +from matcha.models.components.text_encoder import TextEncoder +from matcha.utils.model import ( + denormalize, + duration_loss, + fix_len_compatibility, + generate_path, + sequence_mask, +) + +log = utils.get_pylogger(__name__) + + +class MatchaTTS(BaseLightningClass): # 🍵 + def __init__( + self, + n_vocab, + n_spks, + spk_emb_dim, + n_feats, + encoder, + decoder, + cfm, + data_statistics, + out_size, + optimizer=None, + scheduler=None, + prior_loss=True, + ): + super().__init__() + + self.save_hyperparameters(logger=False) + + self.n_vocab = n_vocab + self.n_spks = n_spks + self.spk_emb_dim = spk_emb_dim + self.n_feats = n_feats + self.out_size = out_size + self.prior_loss = prior_loss + + if n_spks > 1: + self.spk_emb = torch.nn.Embedding(n_spks, spk_emb_dim) + + self.encoder = TextEncoder( + encoder.encoder_type, + encoder.encoder_params, + encoder.duration_predictor_params, + n_vocab, + n_spks, + spk_emb_dim, + ) + + self.decoder = CFM( + in_channels=2 * encoder.encoder_params.n_feats, + out_channel=encoder.encoder_params.n_feats, + cfm_params=cfm, + decoder_params=decoder, + n_spks=n_spks, + spk_emb_dim=spk_emb_dim, + ) + + self.update_data_statistics(data_statistics) + + @torch.inference_mode() + def synthesise(self, x, x_lengths, n_timesteps, temperature=1.0, spks=None, length_scale=1.0): + """ + Generates mel-spectrogram from text. Returns: + 1. encoder outputs + 2. decoder outputs + 3. generated alignment + + Args: + x (torch.Tensor): batch of texts, converted to a tensor with phoneme embedding ids. + shape: (batch_size, max_text_length) + x_lengths (torch.Tensor): lengths of texts in batch. + shape: (batch_size,) + n_timesteps (int): number of steps to use for reverse diffusion in decoder. + temperature (float, optional): controls variance of terminal distribution. + spks (bool, optional): speaker ids. + shape: (batch_size,) + length_scale (float, optional): controls speech pace. + Increase value to slow down generated speech and vice versa. + + Returns: + dict: { + "encoder_outputs": torch.Tensor, shape: (batch_size, n_feats, max_mel_length), + # Average mel spectrogram generated by the encoder + "decoder_outputs": torch.Tensor, shape: (batch_size, n_feats, max_mel_length), + # Refined mel spectrogram improved by the CFM + "attn": torch.Tensor, shape: (batch_size, max_text_length, max_mel_length), + # Alignment map between text and mel spectrogram + "mel": torch.Tensor, shape: (batch_size, n_feats, max_mel_length), + # Denormalized mel spectrogram + "mel_lengths": torch.Tensor, shape: (batch_size,), + # Lengths of mel spectrograms + "rtf": float, + # Real-time factor + """ + # For RTF computation + t = dt.datetime.now() + + if self.n_spks > 1: + # Get speaker embedding + spks = self.spk_emb(spks.long()) + + # Get encoder_outputs `mu_x` and log-scaled token durations `logw` + mu_x, logw, x_mask = self.encoder(x, x_lengths, spks) + + w = torch.exp(logw) * x_mask + w_ceil = torch.ceil(w) * length_scale + y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() + y_max_length = y_lengths.max() + y_max_length_ = fix_len_compatibility(y_max_length) + + # Using obtained durations `w` construct alignment map `attn` + y_mask = sequence_mask(y_lengths, y_max_length_).unsqueeze(1).to(x_mask.dtype) + attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2) + attn = generate_path(w_ceil.squeeze(1), attn_mask.squeeze(1)).unsqueeze(1) + + # Align encoded text and get mu_y + mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2)) + mu_y = mu_y.transpose(1, 2) + encoder_outputs = mu_y[:, :, :y_max_length] + + # Generate sample tracing the probability flow + decoder_outputs = self.decoder(mu_y, y_mask, n_timesteps, temperature, spks) + decoder_outputs = decoder_outputs[:, :, :y_max_length] + + t = (dt.datetime.now() - t).total_seconds() + rtf = t * 22050 / (decoder_outputs.shape[-1] * 256) + + return { + "encoder_outputs": encoder_outputs, + "decoder_outputs": decoder_outputs, + "attn": attn[:, :, :y_max_length], + "mel": denormalize(decoder_outputs, self.mel_mean, self.mel_std), + "mel_lengths": y_lengths, + "rtf": rtf, + } + + def forward(self, x, x_lengths, y, y_lengths, spks=None, out_size=None, cond=None): + """ + Computes 3 losses: + 1. duration loss: loss between predicted token durations and those extracted by Monotinic Alignment Search (MAS). + 2. prior loss: loss between mel-spectrogram and encoder outputs. + 3. flow matching loss: loss between mel-spectrogram and decoder outputs. + + Args: + x (torch.Tensor): batch of texts, converted to a tensor with phoneme embedding ids. + shape: (batch_size, max_text_length) + x_lengths (torch.Tensor): lengths of texts in batch. + shape: (batch_size,) + y (torch.Tensor): batch of corresponding mel-spectrograms. + shape: (batch_size, n_feats, max_mel_length) + y_lengths (torch.Tensor): lengths of mel-spectrograms in batch. + shape: (batch_size,) + out_size (int, optional): length (in mel's sampling rate) of segment to cut, on which decoder will be trained. + Should be divisible by 2^{num of UNet downsamplings}. Needed to increase batch size. + spks (torch.Tensor, optional): speaker ids. + shape: (batch_size,) + """ + if self.n_spks > 1: + # Get speaker embedding + spks = self.spk_emb(spks) + + # Get encoder_outputs `mu_x` and log-scaled token durations `logw` + mu_x, logw, x_mask = self.encoder(x, x_lengths, spks) + y_max_length = y.shape[-1] + + y_mask = sequence_mask(y_lengths, y_max_length).unsqueeze(1).to(x_mask) + attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2) + + # Use MAS to find most likely alignment `attn` between text and mel-spectrogram + with torch.no_grad(): + const = -0.5 * math.log(2 * math.pi) * self.n_feats + factor = -0.5 * torch.ones(mu_x.shape, dtype=mu_x.dtype, device=mu_x.device) + y_square = torch.matmul(factor.transpose(1, 2), y**2) + y_mu_double = torch.matmul(2.0 * (factor * mu_x).transpose(1, 2), y) + mu_square = torch.sum(factor * (mu_x**2), 1).unsqueeze(-1) + log_prior = y_square - y_mu_double + mu_square + const + + attn = monotonic_align.maximum_path(log_prior, attn_mask.squeeze(1)) + attn = attn.detach() + + # Compute loss between predicted log-scaled durations and those obtained from MAS + # refered to as prior loss in the paper + logw_ = torch.log(1e-8 + torch.sum(attn.unsqueeze(1), -1)) * x_mask + dur_loss = duration_loss(logw, logw_, x_lengths) + + # Cut a small segment of mel-spectrogram in order to increase batch size + # - "Hack" taken from Grad-TTS, in case of Grad-TTS, we cannot train batch size 32 on a 24GB GPU without it + # - Do not need this hack for Matcha-TTS, but it works with it as well + if not isinstance(out_size, type(None)): + max_offset = (y_lengths - out_size).clamp(0) + offset_ranges = list(zip([0] * max_offset.shape[0], max_offset.cpu().numpy())) + out_offset = torch.LongTensor( + [torch.tensor(random.choice(range(start, end)) if end > start else 0) for start, end in offset_ranges] + ).to(y_lengths) + attn_cut = torch.zeros(attn.shape[0], attn.shape[1], out_size, dtype=attn.dtype, device=attn.device) + y_cut = torch.zeros(y.shape[0], self.n_feats, out_size, dtype=y.dtype, device=y.device) + + y_cut_lengths = [] + for i, (y_, out_offset_) in enumerate(zip(y, out_offset)): + y_cut_length = out_size + (y_lengths[i] - out_size).clamp(None, 0) + y_cut_lengths.append(y_cut_length) + cut_lower, cut_upper = out_offset_, out_offset_ + y_cut_length + y_cut[i, :, :y_cut_length] = y_[:, cut_lower:cut_upper] + attn_cut[i, :, :y_cut_length] = attn[i, :, cut_lower:cut_upper] + + y_cut_lengths = torch.LongTensor(y_cut_lengths) + y_cut_mask = sequence_mask(y_cut_lengths).unsqueeze(1).to(y_mask) + + attn = attn_cut + y = y_cut + y_mask = y_cut_mask + + # Align encoded text with mel-spectrogram and get mu_y segment + mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2)) + mu_y = mu_y.transpose(1, 2) + + # Compute loss of the decoder + diff_loss, _ = self.decoder.compute_loss(x1=y, mask=y_mask, mu=mu_y, spks=spks, cond=cond) + + if self.prior_loss: + prior_loss = torch.sum(0.5 * ((y - mu_y) ** 2 + math.log(2 * math.pi)) * y_mask) + prior_loss = prior_loss / (torch.sum(y_mask) * self.n_feats) + else: + prior_loss = 0 + + return dur_loss, prior_loss, diff_loss diff --git a/third_party/Matcha-TTS/matcha/onnx/__init__.py b/third_party/Matcha-TTS/matcha/onnx/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/third_party/Matcha-TTS/matcha/onnx/export.py b/third_party/Matcha-TTS/matcha/onnx/export.py new file mode 100644 index 0000000000000000000000000000000000000000..9b795086158e1ad8a4bb5cd92306f3fa765f71ea --- /dev/null +++ b/third_party/Matcha-TTS/matcha/onnx/export.py @@ -0,0 +1,181 @@ +import argparse +import random +from pathlib import Path + +import numpy as np +import torch +from lightning import LightningModule + +from matcha.cli import VOCODER_URLS, load_matcha, load_vocoder + +DEFAULT_OPSET = 15 + +SEED = 1234 +random.seed(SEED) +np.random.seed(SEED) +torch.manual_seed(SEED) +torch.cuda.manual_seed(SEED) +torch.backends.cudnn.deterministic = True +torch.backends.cudnn.benchmark = False + + +class MatchaWithVocoder(LightningModule): + def __init__(self, matcha, vocoder): + super().__init__() + self.matcha = matcha + self.vocoder = vocoder + + def forward(self, x, x_lengths, scales, spks=None): + mel, mel_lengths = self.matcha(x, x_lengths, scales, spks) + wavs = self.vocoder(mel).clamp(-1, 1) + lengths = mel_lengths * 256 + return wavs.squeeze(1), lengths + + +def get_exportable_module(matcha, vocoder, n_timesteps): + """ + Return an appropriate `LighteningModule` and output-node names + based on whether the vocoder is embedded in the final graph + """ + + def onnx_forward_func(x, x_lengths, scales, spks=None): + """ + Custom forward function for accepting + scaler parameters as tensors + """ + # Extract scaler parameters from tensors + temperature = scales[0] + length_scale = scales[1] + output = matcha.synthesise(x, x_lengths, n_timesteps, temperature, spks, length_scale) + return output["mel"], output["mel_lengths"] + + # Monkey-patch Matcha's forward function + matcha.forward = onnx_forward_func + + if vocoder is None: + model, output_names = matcha, ["mel", "mel_lengths"] + else: + model = MatchaWithVocoder(matcha, vocoder) + output_names = ["wav", "wav_lengths"] + return model, output_names + + +def get_inputs(is_multi_speaker): + """ + Create dummy inputs for tracing + """ + dummy_input_length = 50 + x = torch.randint(low=0, high=20, size=(1, dummy_input_length), dtype=torch.long) + x_lengths = torch.LongTensor([dummy_input_length]) + + # Scales + temperature = 0.667 + length_scale = 1.0 + scales = torch.Tensor([temperature, length_scale]) + + model_inputs = [x, x_lengths, scales] + input_names = [ + "x", + "x_lengths", + "scales", + ] + + if is_multi_speaker: + spks = torch.LongTensor([1]) + model_inputs.append(spks) + input_names.append("spks") + + return tuple(model_inputs), input_names + + +def main(): + parser = argparse.ArgumentParser(description="Export 🍵 Matcha-TTS to ONNX") + + parser.add_argument( + "checkpoint_path", + type=str, + help="Path to the model checkpoint", + ) + parser.add_argument("output", type=str, help="Path to output `.onnx` file") + parser.add_argument( + "--n-timesteps", type=int, default=5, help="Number of steps to use for reverse diffusion in decoder (default 5)" + ) + parser.add_argument( + "--vocoder-name", + type=str, + choices=list(VOCODER_URLS.keys()), + default=None, + help="Name of the vocoder to embed in the ONNX graph", + ) + parser.add_argument( + "--vocoder-checkpoint-path", + type=str, + default=None, + help="Vocoder checkpoint to embed in the ONNX graph for an `e2e` like experience", + ) + parser.add_argument("--opset", type=int, default=DEFAULT_OPSET, help="ONNX opset version to use (default 15") + + args = parser.parse_args() + + print(f"[🍵] Loading Matcha checkpoint from {args.checkpoint_path}") + print(f"Setting n_timesteps to {args.n_timesteps}") + + checkpoint_path = Path(args.checkpoint_path) + matcha = load_matcha(checkpoint_path.stem, checkpoint_path, "cpu") + + if args.vocoder_name or args.vocoder_checkpoint_path: + assert ( + args.vocoder_name and args.vocoder_checkpoint_path + ), "Both vocoder_name and vocoder-checkpoint are required when embedding the vocoder in the ONNX graph." + vocoder, _ = load_vocoder(args.vocoder_name, args.vocoder_checkpoint_path, "cpu") + else: + vocoder = None + + is_multi_speaker = matcha.n_spks > 1 + + dummy_input, input_names = get_inputs(is_multi_speaker) + model, output_names = get_exportable_module(matcha, vocoder, args.n_timesteps) + + # Set dynamic shape for inputs/outputs + dynamic_axes = { + "x": {0: "batch_size", 1: "time"}, + "x_lengths": {0: "batch_size"}, + } + + if vocoder is None: + dynamic_axes.update( + { + "mel": {0: "batch_size", 2: "time"}, + "mel_lengths": {0: "batch_size"}, + } + ) + else: + print("Embedding the vocoder in the ONNX graph") + dynamic_axes.update( + { + "wav": {0: "batch_size", 1: "time"}, + "wav_lengths": {0: "batch_size"}, + } + ) + + if is_multi_speaker: + dynamic_axes["spks"] = {0: "batch_size"} + + # Create the output directory (if not exists) + Path(args.output).parent.mkdir(parents=True, exist_ok=True) + + model.to_onnx( + args.output, + dummy_input, + input_names=input_names, + output_names=output_names, + dynamic_axes=dynamic_axes, + opset_version=args.opset, + export_params=True, + do_constant_folding=True, + ) + print(f"[🍵] ONNX model exported to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/third_party/Matcha-TTS/matcha/onnx/infer.py b/third_party/Matcha-TTS/matcha/onnx/infer.py new file mode 100644 index 0000000000000000000000000000000000000000..89ca92559c6df3776a07a038d7838242a3d19189 --- /dev/null +++ b/third_party/Matcha-TTS/matcha/onnx/infer.py @@ -0,0 +1,168 @@ +import argparse +import os +import warnings +from pathlib import Path +from time import perf_counter + +import numpy as np +import onnxruntime as ort +import soundfile as sf +import torch + +from matcha.cli import plot_spectrogram_to_numpy, process_text + + +def validate_args(args): + assert ( + args.text or args.file + ), "Either text or file must be provided Matcha-T(ea)TTS need sometext to whisk the waveforms." + assert args.temperature >= 0, "Sampling temperature cannot be negative" + assert args.speaking_rate >= 0, "Speaking rate must be greater than 0" + return args + + +def write_wavs(model, inputs, output_dir, external_vocoder=None): + if external_vocoder is None: + print("The provided model has the vocoder embedded in the graph.\nGenerating waveform directly") + t0 = perf_counter() + wavs, wav_lengths = model.run(None, inputs) + infer_secs = perf_counter() - t0 + mel_infer_secs = vocoder_infer_secs = None + else: + print("[🍵] Generating mel using Matcha") + mel_t0 = perf_counter() + mels, mel_lengths = model.run(None, inputs) + mel_infer_secs = perf_counter() - mel_t0 + print("Generating waveform from mel using external vocoder") + vocoder_inputs = {external_vocoder.get_inputs()[0].name: mels} + vocoder_t0 = perf_counter() + wavs = external_vocoder.run(None, vocoder_inputs)[0] + vocoder_infer_secs = perf_counter() - vocoder_t0 + wavs = wavs.squeeze(1) + wav_lengths = mel_lengths * 256 + infer_secs = mel_infer_secs + vocoder_infer_secs + + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + for i, (wav, wav_length) in enumerate(zip(wavs, wav_lengths)): + output_filename = output_dir.joinpath(f"output_{i + 1}.wav") + audio = wav[:wav_length] + print(f"Writing audio to {output_filename}") + sf.write(output_filename, audio, 22050, "PCM_24") + + wav_secs = wav_lengths.sum() / 22050 + print(f"Inference seconds: {infer_secs}") + print(f"Generated wav seconds: {wav_secs}") + rtf = infer_secs / wav_secs + if mel_infer_secs is not None: + mel_rtf = mel_infer_secs / wav_secs + print(f"Matcha RTF: {mel_rtf}") + if vocoder_infer_secs is not None: + vocoder_rtf = vocoder_infer_secs / wav_secs + print(f"Vocoder RTF: {vocoder_rtf}") + print(f"Overall RTF: {rtf}") + + +def write_mels(model, inputs, output_dir): + t0 = perf_counter() + mels, mel_lengths = model.run(None, inputs) + infer_secs = perf_counter() - t0 + + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + for i, mel in enumerate(mels): + output_stem = output_dir.joinpath(f"output_{i + 1}") + plot_spectrogram_to_numpy(mel.squeeze(), output_stem.with_suffix(".png")) + np.save(output_stem.with_suffix(".numpy"), mel) + + wav_secs = (mel_lengths * 256).sum() / 22050 + print(f"Inference seconds: {infer_secs}") + print(f"Generated wav seconds: {wav_secs}") + rtf = infer_secs / wav_secs + print(f"RTF: {rtf}") + + +def main(): + parser = argparse.ArgumentParser( + description=" 🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching" + ) + parser.add_argument( + "model", + type=str, + help="ONNX model to use", + ) + parser.add_argument("--vocoder", type=str, default=None, help="Vocoder to use (defaults to None)") + parser.add_argument("--text", type=str, default=None, help="Text to synthesize") + parser.add_argument("--file", type=str, default=None, help="Text file to synthesize") + parser.add_argument("--spk", type=int, default=None, help="Speaker ID") + parser.add_argument( + "--temperature", + type=float, + default=0.667, + help="Variance of the x0 noise (default: 0.667)", + ) + parser.add_argument( + "--speaking-rate", + type=float, + default=1.0, + help="change the speaking rate, a higher value means slower speaking rate (default: 1.0)", + ) + parser.add_argument("--gpu", action="store_true", help="Use CPU for inference (default: use GPU if available)") + parser.add_argument( + "--output-dir", + type=str, + default=os.getcwd(), + help="Output folder to save results (default: current dir)", + ) + + args = parser.parse_args() + args = validate_args(args) + + if args.gpu: + providers = ["GPUExecutionProvider"] + else: + providers = ["CPUExecutionProvider"] + model = ort.InferenceSession(args.model, providers=providers) + + model_inputs = model.get_inputs() + model_outputs = list(model.get_outputs()) + + if args.text: + text_lines = args.text.splitlines() + else: + with open(args.file, encoding="utf-8") as file: + text_lines = file.read().splitlines() + + processed_lines = [process_text(0, line, "cpu") for line in text_lines] + x = [line["x"].squeeze() for line in processed_lines] + # Pad + x = torch.nn.utils.rnn.pad_sequence(x, batch_first=True) + x = x.detach().cpu().numpy() + x_lengths = np.array([line["x_lengths"].item() for line in processed_lines], dtype=np.int64) + inputs = { + "x": x, + "x_lengths": x_lengths, + "scales": np.array([args.temperature, args.speaking_rate], dtype=np.float32), + } + is_multi_speaker = len(model_inputs) == 4 + if is_multi_speaker: + if args.spk is None: + args.spk = 0 + warn = "[!] Speaker ID not provided! Using speaker ID 0" + warnings.warn(warn, UserWarning) + inputs["spks"] = np.repeat(args.spk, x.shape[0]).astype(np.int64) + + has_vocoder_embedded = model_outputs[0].name == "wav" + if has_vocoder_embedded: + write_wavs(model, inputs, args.output_dir) + elif args.vocoder: + external_vocoder = ort.InferenceSession(args.vocoder, providers=providers) + write_wavs(model, inputs, args.output_dir, external_vocoder=external_vocoder) + else: + warn = "[!] A vocoder is not embedded in the graph nor an external vocoder is provided. The mel output will be written as numpy arrays to `*.npy` files in the output directory" + warnings.warn(warn, UserWarning) + write_mels(model, inputs, args.output_dir) + + +if __name__ == "__main__": + main() diff --git a/third_party/Matcha-TTS/matcha/text/__init__.py b/third_party/Matcha-TTS/matcha/text/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..71a4b57891d3c06ad9f25493c1b40bc2f5962d17 --- /dev/null +++ b/third_party/Matcha-TTS/matcha/text/__init__.py @@ -0,0 +1,53 @@ +""" from https://github.com/keithito/tacotron """ +from matcha.text import cleaners +from matcha.text.symbols import symbols + +# Mappings from symbol to numeric ID and vice versa: +_symbol_to_id = {s: i for i, s in enumerate(symbols)} +_id_to_symbol = {i: s for i, s in enumerate(symbols)} # pylint: disable=unnecessary-comprehension + + +def text_to_sequence(text, cleaner_names): + """Converts a string of text to a sequence of IDs corresponding to the symbols in the text. + Args: + text: string to convert to a sequence + cleaner_names: names of the cleaner functions to run the text through + Returns: + List of integers corresponding to the symbols in the text + """ + sequence = [] + + clean_text = _clean_text(text, cleaner_names) + for symbol in clean_text: + symbol_id = _symbol_to_id[symbol] + sequence += [symbol_id] + return sequence + + +def cleaned_text_to_sequence(cleaned_text): + """Converts a string of text to a sequence of IDs corresponding to the symbols in the text. + Args: + text: string to convert to a sequence + Returns: + List of integers corresponding to the symbols in the text + """ + sequence = [_symbol_to_id[symbol] for symbol in cleaned_text] + return sequence + + +def sequence_to_text(sequence): + """Converts a sequence of IDs back to a string""" + result = "" + for symbol_id in sequence: + s = _id_to_symbol[symbol_id] + result += s + return result + + +def _clean_text(text, cleaner_names): + for name in cleaner_names: + cleaner = getattr(cleaners, name) + if not cleaner: + raise Exception("Unknown cleaner: %s" % name) + text = cleaner(text) + return text diff --git a/third_party/Matcha-TTS/matcha/text/cleaners.py b/third_party/Matcha-TTS/matcha/text/cleaners.py new file mode 100644 index 0000000000000000000000000000000000000000..5e8d96b681eb9f57356a1b86a7008e74b65ff44b --- /dev/null +++ b/third_party/Matcha-TTS/matcha/text/cleaners.py @@ -0,0 +1,116 @@ +""" from https://github.com/keithito/tacotron + +Cleaners are transformations that run over the input text at both training and eval time. + +Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners" +hyperparameter. Some cleaners are English-specific. You'll typically want to use: + 1. "english_cleaners" for English text + 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using + the Unidecode library (https://pypi.python.org/pypi/Unidecode) + 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update + the symbols in symbols.py to match your data). +""" + +import logging +import re + +import phonemizer +import piper_phonemize +from unidecode import unidecode + +# To avoid excessive logging we set the log level of the phonemizer package to Critical +critical_logger = logging.getLogger("phonemizer") +critical_logger.setLevel(logging.CRITICAL) + +# Intializing the phonemizer globally significantly reduces the speed +# now the phonemizer is not initialising at every call +# Might be less flexible, but it is much-much faster +global_phonemizer = phonemizer.backend.EspeakBackend( + language="en-us", + preserve_punctuation=True, + with_stress=True, + language_switch="remove-flags", + logger=critical_logger, +) + + +# Regular expression matching whitespace: +_whitespace_re = re.compile(r"\s+") + +# List of (regular expression, replacement) pairs for abbreviations: +_abbreviations = [ + (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1]) + for x in [ + ("mrs", "misess"), + ("mr", "mister"), + ("dr", "doctor"), + ("st", "saint"), + ("co", "company"), + ("jr", "junior"), + ("maj", "major"), + ("gen", "general"), + ("drs", "doctors"), + ("rev", "reverend"), + ("lt", "lieutenant"), + ("hon", "honorable"), + ("sgt", "sergeant"), + ("capt", "captain"), + ("esq", "esquire"), + ("ltd", "limited"), + ("col", "colonel"), + ("ft", "fort"), + ] +] + + +def expand_abbreviations(text): + for regex, replacement in _abbreviations: + text = re.sub(regex, replacement, text) + return text + + +def lowercase(text): + return text.lower() + + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text) + + +def convert_to_ascii(text): + return unidecode(text) + + +def basic_cleaners(text): + """Basic pipeline that lowercases and collapses whitespace without transliteration.""" + text = lowercase(text) + text = collapse_whitespace(text) + return text + + +def transliteration_cleaners(text): + """Pipeline for non-English text that transliterates to ASCII.""" + text = convert_to_ascii(text) + text = lowercase(text) + text = collapse_whitespace(text) + return text + + +def english_cleaners2(text): + """Pipeline for English text, including abbreviation expansion. + punctuation + stress""" + text = convert_to_ascii(text) + text = lowercase(text) + text = expand_abbreviations(text) + phonemes = global_phonemizer.phonemize([text], strip=True, njobs=1)[0] + phonemes = collapse_whitespace(phonemes) + return phonemes + + +def english_cleaners_piper(text): + """Pipeline for English text, including abbreviation expansion. + punctuation + stress""" + text = convert_to_ascii(text) + text = lowercase(text) + text = expand_abbreviations(text) + phonemes = "".join(piper_phonemize.phonemize_espeak(text=text, voice="en-US")[0]) + phonemes = collapse_whitespace(phonemes) + return phonemes diff --git a/third_party/Matcha-TTS/matcha/text/numbers.py b/third_party/Matcha-TTS/matcha/text/numbers.py new file mode 100644 index 0000000000000000000000000000000000000000..f99a8686dcb73532091122613e74bd643a8a327f --- /dev/null +++ b/third_party/Matcha-TTS/matcha/text/numbers.py @@ -0,0 +1,71 @@ +""" from https://github.com/keithito/tacotron """ + +import re + +import inflect + +_inflect = inflect.engine() +_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])") +_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)") +_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)") +_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)") +_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)") +_number_re = re.compile(r"[0-9]+") + + +def _remove_commas(m): + return m.group(1).replace(",", "") + + +def _expand_decimal_point(m): + return m.group(1).replace(".", " point ") + + +def _expand_dollars(m): + match = m.group(1) + parts = match.split(".") + if len(parts) > 2: + return match + " dollars" + dollars = int(parts[0]) if parts[0] else 0 + cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0 + if dollars and cents: + dollar_unit = "dollar" if dollars == 1 else "dollars" + cent_unit = "cent" if cents == 1 else "cents" + return f"{dollars} {dollar_unit}, {cents} {cent_unit}" + elif dollars: + dollar_unit = "dollar" if dollars == 1 else "dollars" + return f"{dollars} {dollar_unit}" + elif cents: + cent_unit = "cent" if cents == 1 else "cents" + return f"{cents} {cent_unit}" + else: + return "zero dollars" + + +def _expand_ordinal(m): + return _inflect.number_to_words(m.group(0)) + + +def _expand_number(m): + num = int(m.group(0)) + if num > 1000 and num < 3000: + if num == 2000: + return "two thousand" + elif num > 2000 and num < 2010: + return "two thousand " + _inflect.number_to_words(num % 100) + elif num % 100 == 0: + return _inflect.number_to_words(num // 100) + " hundred" + else: + return _inflect.number_to_words(num, andword="", zero="oh", group=2).replace(", ", " ") + else: + return _inflect.number_to_words(num, andword="") + + +def normalize_numbers(text): + text = re.sub(_comma_number_re, _remove_commas, text) + text = re.sub(_pounds_re, r"\1 pounds", text) + text = re.sub(_dollars_re, _expand_dollars, text) + text = re.sub(_decimal_number_re, _expand_decimal_point, text) + text = re.sub(_ordinal_re, _expand_ordinal, text) + text = re.sub(_number_re, _expand_number, text) + return text diff --git a/third_party/Matcha-TTS/matcha/text/symbols.py b/third_party/Matcha-TTS/matcha/text/symbols.py new file mode 100644 index 0000000000000000000000000000000000000000..7018df549a1e50c3be20416069b6913c641024bd --- /dev/null +++ b/third_party/Matcha-TTS/matcha/text/symbols.py @@ -0,0 +1,17 @@ +""" from https://github.com/keithito/tacotron + +Defines the set of symbols used in text input to the model. +""" +_pad = "_" +_punctuation = ';:,.!?¡¿—…"«»“” ' +_letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +_letters_ipa = ( + "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ" +) + + +# Export all symbols: +symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa) + +# Special symbol ids +SPACE_ID = symbols.index(" ") diff --git a/third_party/Matcha-TTS/matcha/train.py b/third_party/Matcha-TTS/matcha/train.py new file mode 100644 index 0000000000000000000000000000000000000000..d1d64c6c44af2622be5e6bf368616feb6619ed7e --- /dev/null +++ b/third_party/Matcha-TTS/matcha/train.py @@ -0,0 +1,122 @@ +from typing import Any, Dict, List, Optional, Tuple + +import hydra +import lightning as L +import rootutils +from lightning import Callback, LightningDataModule, LightningModule, Trainer +from lightning.pytorch.loggers import Logger +from omegaconf import DictConfig + +from matcha import utils + +rootutils.setup_root(__file__, indicator=".project-root", pythonpath=True) +# ------------------------------------------------------------------------------------ # +# the setup_root above is equivalent to: +# - adding project root dir to PYTHONPATH +# (so you don't need to force user to install project as a package) +# (necessary before importing any local modules e.g. `from src import utils`) +# - setting up PROJECT_ROOT environment variable +# (which is used as a base for paths in "configs/paths/default.yaml") +# (this way all filepaths are the same no matter where you run the code) +# - loading environment variables from ".env" in root dir +# +# you can remove it if you: +# 1. either install project as a package or move entry files to project root dir +# 2. set `root_dir` to "." in "configs/paths/default.yaml" +# +# more info: https://github.com/ashleve/rootutils +# ------------------------------------------------------------------------------------ # + + +log = utils.get_pylogger(__name__) + + +@utils.task_wrapper +def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]: + """Trains the model. Can additionally evaluate on a testset, using best weights obtained during + training. + + This method is wrapped in optional @task_wrapper decorator, that controls the behavior during + failure. Useful for multiruns, saving info about the crash, etc. + + :param cfg: A DictConfig configuration composed by Hydra. + :return: A tuple with metrics and dict with all instantiated objects. + """ + # set seed for random number generators in pytorch, numpy and python.random + if cfg.get("seed"): + L.seed_everything(cfg.seed, workers=True) + + log.info(f"Instantiating datamodule <{cfg.data._target_}>") # pylint: disable=protected-access + datamodule: LightningDataModule = hydra.utils.instantiate(cfg.data) + + log.info(f"Instantiating model <{cfg.model._target_}>") # pylint: disable=protected-access + model: LightningModule = hydra.utils.instantiate(cfg.model) + + log.info("Instantiating callbacks...") + callbacks: List[Callback] = utils.instantiate_callbacks(cfg.get("callbacks")) + + log.info("Instantiating loggers...") + logger: List[Logger] = utils.instantiate_loggers(cfg.get("logger")) + + log.info(f"Instantiating trainer <{cfg.trainer._target_}>") # pylint: disable=protected-access + trainer: Trainer = hydra.utils.instantiate(cfg.trainer, callbacks=callbacks, logger=logger) + + object_dict = { + "cfg": cfg, + "datamodule": datamodule, + "model": model, + "callbacks": callbacks, + "logger": logger, + "trainer": trainer, + } + + if logger: + log.info("Logging hyperparameters!") + utils.log_hyperparameters(object_dict) + + if cfg.get("train"): + log.info("Starting training!") + trainer.fit(model=model, datamodule=datamodule, ckpt_path=cfg.get("ckpt_path")) + + train_metrics = trainer.callback_metrics + + if cfg.get("test"): + log.info("Starting testing!") + ckpt_path = trainer.checkpoint_callback.best_model_path + if ckpt_path == "": + log.warning("Best ckpt not found! Using current weights for testing...") + ckpt_path = None + trainer.test(model=model, datamodule=datamodule, ckpt_path=ckpt_path) + log.info(f"Best ckpt path: {ckpt_path}") + + test_metrics = trainer.callback_metrics + + # merge train and test metrics + metric_dict = {**train_metrics, **test_metrics} + + return metric_dict, object_dict + + +@hydra.main(version_base="1.3", config_path="../configs", config_name="train.yaml") +def main(cfg: DictConfig) -> Optional[float]: + """Main entry point for training. + + :param cfg: DictConfig configuration composed by Hydra. + :return: Optional[float] with optimized metric value. + """ + # apply extra utilities + # (e.g. ask for tags if none are provided in cfg, print cfg tree, etc.) + utils.extras(cfg) + + # train the model + metric_dict, _ = train(cfg) + + # safely retrieve metric value for hydra-based hyperparameter optimization + metric_value = utils.get_metric_value(metric_dict=metric_dict, metric_name=cfg.get("optimized_metric")) + + # return optimized metric + return metric_value + + +if __name__ == "__main__": + main() # pylint: disable=no-value-for-parameter diff --git a/third_party/Matcha-TTS/matcha/utils/__init__.py b/third_party/Matcha-TTS/matcha/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..074db6461184e8cbb86d977cb41d9ebd918e958a --- /dev/null +++ b/third_party/Matcha-TTS/matcha/utils/__init__.py @@ -0,0 +1,5 @@ +from matcha.utils.instantiators import instantiate_callbacks, instantiate_loggers +from matcha.utils.logging_utils import log_hyperparameters +from matcha.utils.pylogger import get_pylogger +from matcha.utils.rich_utils import enforce_tags, print_config_tree +from matcha.utils.utils import extras, get_metric_value, task_wrapper diff --git a/third_party/Matcha-TTS/matcha/utils/audio.py b/third_party/Matcha-TTS/matcha/utils/audio.py new file mode 100644 index 0000000000000000000000000000000000000000..0bcd74df47fb006f68deb5a5f4a4c2fb0aa84f57 --- /dev/null +++ b/third_party/Matcha-TTS/matcha/utils/audio.py @@ -0,0 +1,82 @@ +import numpy as np +import torch +import torch.utils.data +from librosa.filters import mel as librosa_mel_fn +from scipy.io.wavfile import read + +MAX_WAV_VALUE = 32768.0 + + +def load_wav(full_path): + sampling_rate, data = read(full_path) + return data, sampling_rate + + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) + + +def dynamic_range_decompression(x, C=1): + return np.exp(x) / C + + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def dynamic_range_decompression_torch(x, C=1): + return torch.exp(x) / C + + +def spectral_normalize_torch(magnitudes): + output = dynamic_range_compression_torch(magnitudes) + return output + + +def spectral_de_normalize_torch(magnitudes): + output = dynamic_range_decompression_torch(magnitudes) + return output + + +mel_basis = {} +hann_window = {} + + +def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): + if torch.min(y) < -1.0: + print("min value is ", torch.min(y)) + if torch.max(y) > 1.0: + print("max value is ", torch.max(y)) + + global mel_basis, hann_window # pylint: disable=global-statement + if f"{str(fmax)}_{str(y.device)}" not in mel_basis: + mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax) + mel_basis[str(fmax) + "_" + str(y.device)] = torch.from_numpy(mel).float().to(y.device) + hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device) + + y = torch.nn.functional.pad( + y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect" + ) + y = y.squeeze(1) + + spec = torch.view_as_real( + torch.stft( + y, + n_fft, + hop_length=hop_size, + win_length=win_size, + window=hann_window[str(y.device)], + center=center, + pad_mode="reflect", + normalized=False, + onesided=True, + return_complex=True, + ) + ) + + spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) + + spec = torch.matmul(mel_basis[str(fmax) + "_" + str(y.device)], spec) + spec = spectral_normalize_torch(spec) + + return spec diff --git a/third_party/Matcha-TTS/matcha/utils/generate_data_statistics.py b/third_party/Matcha-TTS/matcha/utils/generate_data_statistics.py new file mode 100644 index 0000000000000000000000000000000000000000..96a5382296426803f1010385d184af7bfc901290 --- /dev/null +++ b/third_party/Matcha-TTS/matcha/utils/generate_data_statistics.py @@ -0,0 +1,111 @@ +r""" +The file creates a pickle file where the values needed for loading of dataset is stored and the model can load it +when needed. + +Parameters from hparam.py will be used +""" +import argparse +import json +import os +import sys +from pathlib import Path + +import rootutils +import torch +from hydra import compose, initialize +from omegaconf import open_dict +from tqdm.auto import tqdm + +from matcha.data.text_mel_datamodule import TextMelDataModule +from matcha.utils.logging_utils import pylogger + +log = pylogger.get_pylogger(__name__) + + +def compute_data_statistics(data_loader: torch.utils.data.DataLoader, out_channels: int): + """Generate data mean and standard deviation helpful in data normalisation + + Args: + data_loader (torch.utils.data.Dataloader): _description_ + out_channels (int): mel spectrogram channels + """ + total_mel_sum = 0 + total_mel_sq_sum = 0 + total_mel_len = 0 + + for batch in tqdm(data_loader, leave=False): + mels = batch["y"] + mel_lengths = batch["y_lengths"] + + total_mel_len += torch.sum(mel_lengths) + total_mel_sum += torch.sum(mels) + total_mel_sq_sum += torch.sum(torch.pow(mels, 2)) + + data_mean = total_mel_sum / (total_mel_len * out_channels) + data_std = torch.sqrt((total_mel_sq_sum / (total_mel_len * out_channels)) - torch.pow(data_mean, 2)) + + return {"mel_mean": data_mean.item(), "mel_std": data_std.item()} + + +def main(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "-i", + "--input-config", + type=str, + default="vctk.yaml", + help="The name of the yaml config file under configs/data", + ) + + parser.add_argument( + "-b", + "--batch-size", + type=int, + default="256", + help="Can have increased batch size for faster computation", + ) + + parser.add_argument( + "-f", + "--force", + action="store_true", + default=False, + required=False, + help="force overwrite the file", + ) + args = parser.parse_args() + output_file = Path(args.input_config).with_suffix(".json") + + if os.path.exists(output_file) and not args.force: + print("File already exists. Use -f to force overwrite") + sys.exit(1) + + with initialize(version_base="1.3", config_path="../../configs/data"): + cfg = compose(config_name=args.input_config, return_hydra_config=True, overrides=[]) + + root_path = rootutils.find_root(search_from=__file__, indicator=".project-root") + + with open_dict(cfg): + del cfg["hydra"] + del cfg["_target_"] + cfg["data_statistics"] = None + cfg["seed"] = 1234 + cfg["batch_size"] = args.batch_size + cfg["train_filelist_path"] = str(os.path.join(root_path, cfg["train_filelist_path"])) + cfg["valid_filelist_path"] = str(os.path.join(root_path, cfg["valid_filelist_path"])) + + text_mel_datamodule = TextMelDataModule(**cfg) + text_mel_datamodule.setup() + data_loader = text_mel_datamodule.train_dataloader() + log.info("Dataloader loaded! Now computing stats...") + params = compute_data_statistics(data_loader, cfg["n_feats"]) + print(params) + json.dump( + params, + open(output_file, "w"), + ) + + +if __name__ == "__main__": + main() diff --git a/third_party/Matcha-TTS/matcha/utils/instantiators.py b/third_party/Matcha-TTS/matcha/utils/instantiators.py new file mode 100644 index 0000000000000000000000000000000000000000..5547b4ed61ed8c21e63c528f58526a949879a94f --- /dev/null +++ b/third_party/Matcha-TTS/matcha/utils/instantiators.py @@ -0,0 +1,56 @@ +from typing import List + +import hydra +from lightning import Callback +from lightning.pytorch.loggers import Logger +from omegaconf import DictConfig + +from matcha.utils import pylogger + +log = pylogger.get_pylogger(__name__) + + +def instantiate_callbacks(callbacks_cfg: DictConfig) -> List[Callback]: + """Instantiates callbacks from config. + + :param callbacks_cfg: A DictConfig object containing callback configurations. + :return: A list of instantiated callbacks. + """ + callbacks: List[Callback] = [] + + if not callbacks_cfg: + log.warning("No callback configs found! Skipping..") + return callbacks + + if not isinstance(callbacks_cfg, DictConfig): + raise TypeError("Callbacks config must be a DictConfig!") + + for _, cb_conf in callbacks_cfg.items(): + if isinstance(cb_conf, DictConfig) and "_target_" in cb_conf: + log.info(f"Instantiating callback <{cb_conf._target_}>") # pylint: disable=protected-access + callbacks.append(hydra.utils.instantiate(cb_conf)) + + return callbacks + + +def instantiate_loggers(logger_cfg: DictConfig) -> List[Logger]: + """Instantiates loggers from config. + + :param logger_cfg: A DictConfig object containing logger configurations. + :return: A list of instantiated loggers. + """ + logger: List[Logger] = [] + + if not logger_cfg: + log.warning("No logger configs found! Skipping...") + return logger + + if not isinstance(logger_cfg, DictConfig): + raise TypeError("Logger config must be a DictConfig!") + + for _, lg_conf in logger_cfg.items(): + if isinstance(lg_conf, DictConfig) and "_target_" in lg_conf: + log.info(f"Instantiating logger <{lg_conf._target_}>") # pylint: disable=protected-access + logger.append(hydra.utils.instantiate(lg_conf)) + + return logger diff --git a/third_party/Matcha-TTS/matcha/utils/logging_utils.py b/third_party/Matcha-TTS/matcha/utils/logging_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1a12d1ddafa25ca3ae8e497bcd7de2191f13659b --- /dev/null +++ b/third_party/Matcha-TTS/matcha/utils/logging_utils.py @@ -0,0 +1,53 @@ +from typing import Any, Dict + +from lightning.pytorch.utilities import rank_zero_only +from omegaconf import OmegaConf + +from matcha.utils import pylogger + +log = pylogger.get_pylogger(__name__) + + +@rank_zero_only +def log_hyperparameters(object_dict: Dict[str, Any]) -> None: + """Controls which config parts are saved by Lightning loggers. + + Additionally saves: + - Number of model parameters + + :param object_dict: A dictionary containing the following objects: + - `"cfg"`: A DictConfig object containing the main config. + - `"model"`: The Lightning model. + - `"trainer"`: The Lightning trainer. + """ + hparams = {} + + cfg = OmegaConf.to_container(object_dict["cfg"]) + model = object_dict["model"] + trainer = object_dict["trainer"] + + if not trainer.logger: + log.warning("Logger not found! Skipping hyperparameter logging...") + return + + hparams["model"] = cfg["model"] + + # save number of model parameters + hparams["model/params/total"] = sum(p.numel() for p in model.parameters()) + hparams["model/params/trainable"] = sum(p.numel() for p in model.parameters() if p.requires_grad) + hparams["model/params/non_trainable"] = sum(p.numel() for p in model.parameters() if not p.requires_grad) + + hparams["data"] = cfg["data"] + hparams["trainer"] = cfg["trainer"] + + hparams["callbacks"] = cfg.get("callbacks") + hparams["extras"] = cfg.get("extras") + + hparams["task_name"] = cfg.get("task_name") + hparams["tags"] = cfg.get("tags") + hparams["ckpt_path"] = cfg.get("ckpt_path") + hparams["seed"] = cfg.get("seed") + + # send hparams to all loggers + for logger in trainer.loggers: + logger.log_hyperparams(hparams) diff --git a/third_party/Matcha-TTS/matcha/utils/model.py b/third_party/Matcha-TTS/matcha/utils/model.py new file mode 100644 index 0000000000000000000000000000000000000000..869cc6092f5952930534c47544fae88308e96abf --- /dev/null +++ b/third_party/Matcha-TTS/matcha/utils/model.py @@ -0,0 +1,90 @@ +""" from https://github.com/jaywalnut310/glow-tts """ + +import numpy as np +import torch + + +def sequence_mask(length, max_length=None): + if max_length is None: + max_length = length.max() + x = torch.arange(max_length, dtype=length.dtype, device=length.device) + return x.unsqueeze(0) < length.unsqueeze(1) + + +def fix_len_compatibility(length, num_downsamplings_in_unet=2): + factor = torch.scalar_tensor(2).pow(num_downsamplings_in_unet) + length = (length / factor).ceil() * factor + if not torch.onnx.is_in_onnx_export(): + return length.int().item() + else: + return length + + +def convert_pad_shape(pad_shape): + inverted_shape = pad_shape[::-1] + pad_shape = [item for sublist in inverted_shape for item in sublist] + return pad_shape + + +def generate_path(duration, mask): + device = duration.device + + b, t_x, t_y = mask.shape + cum_duration = torch.cumsum(duration, 1) + path = torch.zeros(b, t_x, t_y, dtype=mask.dtype).to(device=device) + + cum_duration_flat = cum_duration.view(b * t_x) + path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) + path = path.view(b, t_x, t_y) + path = path - torch.nn.functional.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] + path = path * mask + return path + + +def duration_loss(logw, logw_, lengths): + loss = torch.sum((logw - logw_) ** 2) / torch.sum(lengths) + return loss + + +def normalize(data, mu, std): + if not isinstance(mu, (float, int)): + if isinstance(mu, list): + mu = torch.tensor(mu, dtype=data.dtype, device=data.device) + elif isinstance(mu, torch.Tensor): + mu = mu.to(data.device) + elif isinstance(mu, np.ndarray): + mu = torch.from_numpy(mu).to(data.device) + mu = mu.unsqueeze(-1) + + if not isinstance(std, (float, int)): + if isinstance(std, list): + std = torch.tensor(std, dtype=data.dtype, device=data.device) + elif isinstance(std, torch.Tensor): + std = std.to(data.device) + elif isinstance(std, np.ndarray): + std = torch.from_numpy(std).to(data.device) + std = std.unsqueeze(-1) + + return (data - mu) / std + + +def denormalize(data, mu, std): + if not isinstance(mu, float): + if isinstance(mu, list): + mu = torch.tensor(mu, dtype=data.dtype, device=data.device) + elif isinstance(mu, torch.Tensor): + mu = mu.to(data.device) + elif isinstance(mu, np.ndarray): + mu = torch.from_numpy(mu).to(data.device) + mu = mu.unsqueeze(-1) + + if not isinstance(std, float): + if isinstance(std, list): + std = torch.tensor(std, dtype=data.dtype, device=data.device) + elif isinstance(std, torch.Tensor): + std = std.to(data.device) + elif isinstance(std, np.ndarray): + std = torch.from_numpy(std).to(data.device) + std = std.unsqueeze(-1) + + return data * std + mu diff --git a/third_party/Matcha-TTS/matcha/utils/monotonic_align/__init__.py b/third_party/Matcha-TTS/matcha/utils/monotonic_align/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..eee6e0d47c2e3612ef02bc17442e6886998e5a94 --- /dev/null +++ b/third_party/Matcha-TTS/matcha/utils/monotonic_align/__init__.py @@ -0,0 +1,22 @@ +import numpy as np +import torch + +from matcha.utils.monotonic_align.core import maximum_path_c + + +def maximum_path(value, mask): + """Cython optimised version. + value: [b, t_x, t_y] + mask: [b, t_x, t_y] + """ + value = value * mask + device = value.device + dtype = value.dtype + value = value.data.cpu().numpy().astype(np.float32) + path = np.zeros_like(value).astype(np.int32) + mask = mask.data.cpu().numpy() + + t_x_max = mask.sum(1)[:, 0].astype(np.int32) + t_y_max = mask.sum(2)[:, 0].astype(np.int32) + maximum_path_c(path, value, t_x_max, t_y_max) + return torch.from_numpy(path).to(device=device, dtype=dtype) diff --git a/third_party/Matcha-TTS/matcha/utils/monotonic_align/core.pyx b/third_party/Matcha-TTS/matcha/utils/monotonic_align/core.pyx new file mode 100644 index 0000000000000000000000000000000000000000..091fcc3a50a51f3d3fee47a70825260757e6d885 --- /dev/null +++ b/third_party/Matcha-TTS/matcha/utils/monotonic_align/core.pyx @@ -0,0 +1,47 @@ +import numpy as np + +cimport cython +cimport numpy as np + +from cython.parallel import prange + + +@cython.boundscheck(False) +@cython.wraparound(False) +cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_x, int t_y, float max_neg_val) nogil: + cdef int x + cdef int y + cdef float v_prev + cdef float v_cur + cdef float tmp + cdef int index = t_x - 1 + + for y in range(t_y): + for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)): + if x == y: + v_cur = max_neg_val + else: + v_cur = value[x, y-1] + if x == 0: + if y == 0: + v_prev = 0. + else: + v_prev = max_neg_val + else: + v_prev = value[x-1, y-1] + value[x, y] = max(v_cur, v_prev) + value[x, y] + + for y in range(t_y - 1, -1, -1): + path[index, y] = 1 + if index != 0 and (index == y or value[index, y-1] < value[index-1, y-1]): + index = index - 1 + + +@cython.boundscheck(False) +@cython.wraparound(False) +cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_xs, int[::1] t_ys, float max_neg_val=-1e9) nogil: + cdef int b = values.shape[0] + + cdef int i + for i in prange(b, nogil=True): + maximum_path_each(paths[i], values[i], t_xs[i], t_ys[i], max_neg_val) diff --git a/third_party/Matcha-TTS/matcha/utils/monotonic_align/setup.py b/third_party/Matcha-TTS/matcha/utils/monotonic_align/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..f22bc6a35a5a04c9e6d7b82040973722c9b770c9 --- /dev/null +++ b/third_party/Matcha-TTS/matcha/utils/monotonic_align/setup.py @@ -0,0 +1,7 @@ +# from distutils.core import setup +# from Cython.Build import cythonize +# import numpy + +# setup(name='monotonic_align', +# ext_modules=cythonize("core.pyx"), +# include_dirs=[numpy.get_include()]) diff --git a/third_party/Matcha-TTS/matcha/utils/pylogger.py b/third_party/Matcha-TTS/matcha/utils/pylogger.py new file mode 100644 index 0000000000000000000000000000000000000000..61600678029362e110f655edb91d5f3bc5b1cd1c --- /dev/null +++ b/third_party/Matcha-TTS/matcha/utils/pylogger.py @@ -0,0 +1,21 @@ +import logging + +from lightning.pytorch.utilities import rank_zero_only + + +def get_pylogger(name: str = __name__) -> logging.Logger: + """Initializes a multi-GPU-friendly python command line logger. + + :param name: The name of the logger, defaults to ``__name__``. + + :return: A logger object. + """ + logger = logging.getLogger(name) + + # this ensures all logging levels get marked with the rank zero decorator + # otherwise logs would get multiplied for each GPU process in multi-GPU setup + logging_levels = ("debug", "info", "warning", "error", "exception", "fatal", "critical") + for level in logging_levels: + setattr(logger, level, rank_zero_only(getattr(logger, level))) + + return logger diff --git a/third_party/Matcha-TTS/matcha/utils/rich_utils.py b/third_party/Matcha-TTS/matcha/utils/rich_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f602f6e9351d948946eb419eb4e420190ea634bc --- /dev/null +++ b/third_party/Matcha-TTS/matcha/utils/rich_utils.py @@ -0,0 +1,101 @@ +from pathlib import Path +from typing import Sequence + +import rich +import rich.syntax +import rich.tree +from hydra.core.hydra_config import HydraConfig +from lightning.pytorch.utilities import rank_zero_only +from omegaconf import DictConfig, OmegaConf, open_dict +from rich.prompt import Prompt + +from matcha.utils import pylogger + +log = pylogger.get_pylogger(__name__) + + +@rank_zero_only +def print_config_tree( + cfg: DictConfig, + print_order: Sequence[str] = ( + "data", + "model", + "callbacks", + "logger", + "trainer", + "paths", + "extras", + ), + resolve: bool = False, + save_to_file: bool = False, +) -> None: + """Prints the contents of a DictConfig as a tree structure using the Rich library. + + :param cfg: A DictConfig composed by Hydra. + :param print_order: Determines in what order config components are printed. Default is ``("data", "model", + "callbacks", "logger", "trainer", "paths", "extras")``. + :param resolve: Whether to resolve reference fields of DictConfig. Default is ``False``. + :param save_to_file: Whether to export config to the hydra output folder. Default is ``False``. + """ + style = "dim" + tree = rich.tree.Tree("CONFIG", style=style, guide_style=style) + + queue = [] + + # add fields from `print_order` to queue + for field in print_order: + _ = ( + queue.append(field) + if field in cfg + else log.warning(f"Field '{field}' not found in config. Skipping '{field}' config printing...") + ) + + # add all the other fields to queue (not specified in `print_order`) + for field in cfg: + if field not in queue: + queue.append(field) + + # generate config tree from queue + for field in queue: + branch = tree.add(field, style=style, guide_style=style) + + config_group = cfg[field] + if isinstance(config_group, DictConfig): + branch_content = OmegaConf.to_yaml(config_group, resolve=resolve) + else: + branch_content = str(config_group) + + branch.add(rich.syntax.Syntax(branch_content, "yaml")) + + # print config tree + rich.print(tree) + + # save config tree to file + if save_to_file: + with open(Path(cfg.paths.output_dir, "config_tree.log"), "w") as file: + rich.print(tree, file=file) + + +@rank_zero_only +def enforce_tags(cfg: DictConfig, save_to_file: bool = False) -> None: + """Prompts user to input tags from command line if no tags are provided in config. + + :param cfg: A DictConfig composed by Hydra. + :param save_to_file: Whether to export tags to the hydra output folder. Default is ``False``. + """ + if not cfg.get("tags"): + if "id" in HydraConfig().cfg.hydra.job: + raise ValueError("Specify tags before launching a multirun!") + + log.warning("No tags provided in config. Prompting user to input tags...") + tags = Prompt.ask("Enter a list of comma separated tags", default="dev") + tags = [t.strip() for t in tags.split(",") if t != ""] + + with open_dict(cfg): + cfg.tags = tags + + log.info(f"Tags: {cfg.tags}") + + if save_to_file: + with open(Path(cfg.paths.output_dir, "tags.log"), "w") as file: + rich.print(cfg.tags, file=file) diff --git a/third_party/Matcha-TTS/matcha/utils/utils.py b/third_party/Matcha-TTS/matcha/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..af65e09070b4a4786ad139ec6e3d57d5ef578204 --- /dev/null +++ b/third_party/Matcha-TTS/matcha/utils/utils.py @@ -0,0 +1,219 @@ +import os +import sys +import warnings +from importlib.util import find_spec +from pathlib import Path +from typing import Any, Callable, Dict, Tuple + +import gdown +import matplotlib.pyplot as plt +import numpy as np +import torch +import wget +from omegaconf import DictConfig + +from matcha.utils import pylogger, rich_utils + +log = pylogger.get_pylogger(__name__) + + +def extras(cfg: DictConfig) -> None: + """Applies optional utilities before the task is started. + + Utilities: + - Ignoring python warnings + - Setting tags from command line + - Rich config printing + + :param cfg: A DictConfig object containing the config tree. + """ + # return if no `extras` config + if not cfg.get("extras"): + log.warning("Extras config not found!