app.py

#!/usr/bin/env pythonvv

import os
import pathlib
import tempfile

import gradio as gr
import torch
import torchaudio
from fairseq2.assets import InProcAssetMetadataProvider, asset_store
from fairseq2.data import Collater, SequenceData, VocabularyInfo
from fairseq2.data.audio import (
    AudioDecoder,
    WaveformToFbankConverter,
    WaveformToFbankOutput,
)

from seamless_communication.inference import SequenceGeneratorOptions
from fairseq2.generation import NGramRepeatBlockProcessor
from fairseq2.memory import MemoryBlock
from fairseq2.typing import DataType, Device
from huggingface_hub import snapshot_download
from seamless_communication.inference import BatchedSpeechOutput, Translator, SequenceGeneratorOptions
from seamless_communication.models.generator.loader import load_pretssel_vocoder_model
from seamless_communication.models.unity import (
    UnitTokenizer,
    load_gcmvn_stats,
    load_unity_text_tokenizer,
    load_unity_unit_tokenizer,
)
from torch.nn import Module
from seamless_communication.cli.expressivity.evaluate.pretssel_inference_helper import PretsselGenerator

from utils import LANGUAGE_CODE_TO_NAME

DESCRIPTION = """\
# Seamless Expressive


[SeamlessExpressive](https://github.com/facebookresearch/seamless_communication/blob/main/docs/expressive/README.md) is a speech-to-speech translation model that captures certain underexplored aspects of prosody such as speech rate and pauses, while preserving the style of one's voice and high content translation quality. The model is also in use on the [SeamlessExpressive demo website](https://seamless.metademolab.com/expressive?utm_source=huggingface&utm_medium=web&utm_campaign=seamless&utm_content=expressivespace).
"""

CACHE_EXAMPLES = os.getenv("CACHE_EXAMPLES") == "1" and torch.cuda.is_available()

CHECKPOINTS_PATH = pathlib.Path(os.getenv("CHECKPOINTS_PATH", "/home/user/app/models"))
if not CHECKPOINTS_PATH.exists():
    snapshot_download(repo_id="facebook/seamless-expressive", repo_type="model", local_dir=CHECKPOINTS_PATH)
    snapshot_download(repo_id="facebook/seamless-m4t-v2-large", repo_type="model", local_dir=CHECKPOINTS_PATH)

# Ensure that we do not have any other environment resolvers and always return
# "demo" for demo purposes.
asset_store.env_resolvers.clear()
asset_store.env_resolvers.append(lambda: "demo")

# Construct an `InProcAssetMetadataProvider` with environment-specific metadata
# that just overrides the regular metadata for "demo" environment. Note the "@demo" suffix.
demo_metadata = [
    {
        "name": "seamless_expressivity@demo",
        "checkpoint": f"file://{CHECKPOINTS_PATH}/m2m_expressive_unity.pt",
        "char_tokenizer": f"file://{CHECKPOINTS_PATH}/spm_char_lang38_tc.model",
    },
    {
        "name": "vocoder_pretssel@demo",
        "checkpoint": f"file://{CHECKPOINTS_PATH}/pretssel_melhifigan_wm-final.pt",
    },
    {
        "name": "seamlessM4T_v2_large@demo",
        "checkpoint": f"file://{CHECKPOINTS_PATH}/seamlessM4T_v2_large.pt",
        "char_tokenizer": f"file://{CHECKPOINTS_PATH}/spm_char_lang38_tc.model",
    },
]

asset_store.metadata_providers.append(InProcAssetMetadataProvider(demo_metadata))

LANGUAGE_NAME_TO_CODE = {v: k for k, v in LANGUAGE_CODE_TO_NAME.items()}


if torch.cuda.is_available():
    device = torch.device("cuda:0")
    dtype = torch.float16
else:
    device = torch.device("cpu")
    dtype = torch.float32


MODEL_NAME = "seamless_expressivity"
VOCODER_NAME = "vocoder_pretssel"

# used for ASR for toxicity
m4t_translator = Translator(
    model_name_or_card="seamlessM4T_v2_large",
    vocoder_name_or_card=None,
    device=device,
    dtype=dtype,
)
unit_tokenizer = load_unity_unit_tokenizer(MODEL_NAME)

_gcmvn_mean, _gcmvn_std = load_gcmvn_stats(VOCODER_NAME)
gcmvn_mean = torch.tensor(_gcmvn_mean, device=device, dtype=dtype)
gcmvn_std = torch.tensor(_gcmvn_std, device=device, dtype=dtype)

translator = Translator(
    MODEL_NAME,
    vocoder_name_or_card=None,
    device=device,
    dtype=dtype,
    apply_mintox=False,
)

text_generation_opts = SequenceGeneratorOptions(
    beam_size=5,
    unk_penalty=torch.inf,
    soft_max_seq_len=(0, 200),
    step_processor=NGramRepeatBlockProcessor(
        ngram_size=10,
    ),
)
m4t_text_generation_opts = SequenceGeneratorOptions(
    beam_size=5,
    unk_penalty=torch.inf,
    soft_max_seq_len=(1, 200),
    step_processor=NGramRepeatBlockProcessor(
        ngram_size=10,
    ),
)

pretssel_generator = PretsselGenerator(
    VOCODER_NAME,
    vocab_info=unit_tokenizer.vocab_info,
    device=device,
    dtype=dtype,
)

decode_audio = AudioDecoder(dtype=torch.float32, device=device)

convert_to_fbank = WaveformToFbankConverter(
    num_mel_bins=80,
    waveform_scale=2**15,
    channel_last=True,
    standardize=False,
    device=device,
    dtype=dtype,
)


def normalize_fbank(data: WaveformToFbankOutput) -> WaveformToFbankOutput:
    fbank = data["fbank"]
    std, mean = torch.std_mean(fbank, dim=0)
    data["fbank"] = fbank.subtract(mean).divide(std)
    data["gcmvn_fbank"] = fbank.subtract(gcmvn_mean).divide(gcmvn_std)
    return data


collate = Collater(pad_value=0, pad_to_multiple=1)


AUDIO_SAMPLE_RATE = 16000
MAX_INPUT_AUDIO_LENGTH = 10  # in seconds


def remove_prosody_tokens_from_text(text):
    # filter out prosody tokens, there is only emphasis '*', and pause '='
    text = text.replace("*", "").replace("=", "")
    text = " ".join(text.split())
    return text


def preprocess_audio(input_audio_path: str) -> None:
    arr, org_sr = torchaudio.load(input_audio_path)
    new_arr = torchaudio.functional.resample(arr, orig_freq=org_sr, new_freq=AUDIO_SAMPLE_RATE)
    max_length = int(MAX_INPUT_AUDIO_LENGTH * AUDIO_SAMPLE_RATE)
    if new_arr.shape[1] > max_length:
        new_arr = new_arr[:, :max_length]
        gr.Warning(f"Input audio is too long. Only the first {MAX_INPUT_AUDIO_LENGTH} seconds is used.")
    torchaudio.save(input_audio_path, new_arr, sample_rate=AUDIO_SAMPLE_RATE)


def run(
    input_audio_path: str,
    source_language: str,
    target_language: str,
) -> tuple[str, str]:
    target_language_code = LANGUAGE_NAME_TO_CODE[target_language]
    source_language_code = LANGUAGE_NAME_TO_CODE[source_language]

    preprocess_audio(input_audio_path)

    with pathlib.Path(input_audio_path).open("rb") as fb:
        block = MemoryBlock(fb.read())
        example = decode_audio(block)

    example = convert_to_fbank(example)
    example = normalize_fbank(example)
    example = collate(example)

    # get transcription for mintox
    source_sentences, _ = m4t_translator.predict(
        input=example["fbank"],
        task_str="S2TT",  # get source text
        tgt_lang=source_language_code,
        text_generation_opts=m4t_text_generation_opts,
    )
    source_text = str(source_sentences[0])

    prosody_encoder_input = example["gcmvn_fbank"]
    text_output, unit_output = translator.predict(
        example["fbank"],
        "S2ST",
        tgt_lang=target_language_code,
        src_lang=source_language_code,
        text_generation_opts=text_generation_opts,
        unit_generation_ngram_filtering=False,
        duration_factor=1.0,
        prosody_encoder_input=prosody_encoder_input,
        src_text=source_text,  # for mintox check
    )
    speech_output = pretssel_generator.predict(
        unit_output.units,
        tgt_lang=target_language_code,
        prosody_encoder_input=prosody_encoder_input,
    )

    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
        torchaudio.save(
            f.name,
            speech_output.audio_wavs[0][0].to(torch.float32).cpu(),
            sample_rate=speech_output.sample_rate,
        )

    text_out = remove_prosody_tokens_from_text(str(text_output[0]))

    return f.name, text_out


TARGET_LANGUAGE_NAMES = [
    "English",
    "French",
    "German",
    "Spanish",
]

UPDATED_LANGUAGE_LIST = {
    "English": ["French", "German", "Spanish"],
    "French": ["English", "German", "Spanish"],
    "German": ["English", "French", "Spanish"],
    "Spanish": ["English", "French", "German"],
}

def rs_change(rs):
    return gr.update(
        choices=UPDATED_LANGUAGE_LIST[rs],
        value=UPDATED_LANGUAGE_LIST[rs][0],
    )

with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(
        value="Duplicate Space for private use",
        elem_id="duplicate-button",
        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
    )
    with gr.Row():
        with gr.Column():
            with gr.Group():
                input_audio = gr.Audio(label="Input speech", type="filepath")
                source_language = gr.Dropdown(
                    label="Source language",
                    choices=TARGET_LANGUAGE_NAMES,
                    value="English",
                )
                target_language = gr.Dropdown(
                    label="Target language",
                    choices=TARGET_LANGUAGE_NAMES,
                    value="French",
                    interactive=True,
                )
                source_language.change(
                    fn=rs_change, 
                    inputs=[source_language], 
                    outputs=[target_language],
                )
                
            btn = gr.Button()
        with gr.Column():
            with gr.Group():
                output_audio = gr.Audio(label="Translated speech")
                output_text = gr.Textbox(label="Translated text")

    gr.Examples(
        examples=[
            ["assets/Excited-English.wav", "English", "Spanish"],
            ["assets/Whisper-English.wav", "English", "German"],
            ["assets/FastTalking-French.wav", "French", "English"],
            ["assets/Sad-English.wav", "English", "Spanish"],
        ],
        inputs=[input_audio, source_language, target_language],
        outputs=[output_audio, output_text],
        fn=run,
        cache_examples=CACHE_EXAMPLES,
        api_name=False,
    )

    btn.click(
        fn=run,
        inputs=[input_audio, source_language, target_language],
        outputs=[output_audio, output_text],
        api_name="run",
    )

if __name__ == "__main__":
    demo.queue(max_size=50).launch()