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# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/5A. T2S dataset preparation.ipynb.
# %% auto 0
__all__ = []
# %% ../nbs/5A. T2S dataset preparation.ipynb 2
import sys
import os
import itertools
from pathlib import Path
import numpy as np
import torch
import torchaudio
import torch.nn.functional as F
from torch.profiler import profile, record_function, ProfilerActivity
from fastprogress import progress_bar
from fastcore.script import *
import whisper, whisperx
from . import vad, wh_transcribe, vq_stoks, extract_acoustic
import webdataset as wds
# %% ../nbs/5A. T2S dataset preparation.ipynb 4
def flac_to_t2s_name(input):
return input.rsplit("/", 1)[1].replace('flac', 't2s') + ".gz"
# %% ../nbs/5A. T2S dataset preparation.ipynb 6
class Transcriber:
"""
A helper class to transcribe a batch of 30 second audio chunks.
"""
def __init__(self, model_size, lang=False):
self.model = whisperx.asr.load_model(model_size, "cuda", compute_type="float16", language=lang)
# without calling vad_model at least once the rest segfaults for some reason...
self.model.vad_model({"waveform": torch.zeros(1, 16000), "sample_rate": 16000})
def transcribe(self, batch):
batch = whisper.log_mel_spectrogram(batch)
embs = self.model.model.encode(batch.cpu().numpy())
return self.model.tokenizer.tokenizer.decode_batch([x.sequences_ids[0] for x in
self.model.model.model.generate(
embs,
[self.model.model.get_prompt(self.model.tokenizer, [], without_timestamps=True)]*len(batch),
)])
# %% ../nbs/5A. T2S dataset preparation.ipynb 7
@call_parse
def prepare_t2s(
input:str, # FLAC webdataset file path (or - to read the names from stdin)
proc_dataset_path:Path, # processed VAD files path
output:str=None, # output file name
vq_model:str="collabora/spear-tts-pytorch:whisper-vq-stoks.model", # the model path (use repo_id:filename to download it from hugginface)
n_samples:int=None, # process a limited amount of samples
batch_size:int=1, # process several segments at once
transcription_model:str="small.en",
):
if ":" in vq_model:
repo, fname = vq_model.split(":", 1)
vq_model = vq_stoks.RQBottleneckTransformer.load_model(repo, fname).cuda()
else:
vq_model = vq_stoks.RQBottleneckTransformer.load_model(local_filename=vq_model).cuda()
transcriber = Transcriber(transcription_model)
if input == "-":
input = [f.strip() for f in sys.stdin.readlines()]
assert output, "please provide the output shard name"
else:
if output is None: output = flac_to_t2s_name(input)
input = [input]
total = n_samples//batch_size if n_samples else 'noinfer'
if n_samples: print(f"Benchmarking run of {n_samples} samples ({total} batches)")
ds = wds.WebDataset(input, shardshuffle=True, rename_files=vad.fix_dots_in_names).compose(
wds.decode(wds.torch_audio),
vq_stoks.merge_in(vq_stoks.derived_dataset(proc_dataset_path, 'vad')),
wds.map_dict(**{"vad.npy": lambda s: wh_transcribe.chunk_merger(s, wh_transcribe.random_cutter)}),
lambda x: wh_transcribe.split_to_chunks(x),
# drop the first and last segment because they tend to be inaccurate
# (the transcriptions don't have the "LibriVox" header and "end of chapter" suffix)
wds.select(lambda x: x['i'] != 0 and x['i'] != x['imax']),
wds.to_tuple('__key__', 'rpad', 'samples'),
wds.batched(64),
)
dl = wds.WebLoader(ds, num_workers=4, batch_size=None).unbatched().shuffle(2000).batched(batch_size)
speakers = set()
tmp = output+".tmp"
with wds.TarWriter(tmp) as sink:
for keys, rpads, samples in progress_bar(dl, total=total):
with record_function('to_cuda'):
csamples = samples.cuda()
with record_function('transcribe'):
txts = transcriber.transcribe(csamples)
with record_function('vq_stoks'):
stoks = vq_model.encode_audio(csamples)
with record_function('from_cuda'):
stoks = stoks.cpu().numpy().astype(np.int16)
for key, rpad, txt, _stoks in zip(keys, rpads, txts, stoks):
speakers.add(key.split('/')[1])
sink.write({
"__key__": key,
"txt": txt,
"stoks.npy": _stoks[:int(-rpad/16000 * 25)],
})
with open(output+".speakers.txt", "w") as f: f.write("\n".join(speakers))
if not n_samples:
os.rename(tmp, output)
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