app.py

 #!/usr/bin/env python
# Copyright (c) Meta Platforms, Inc. and affiliates
# All rights reserved.
#
# This source code is licensed under the license found in the
# MIT_LICENSE file in the root directory of this source tree.

import os
import pathlib
import tempfile
from pydub import AudioSegment, silence
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) 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.
"""

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

CHECKPOINTS_PATH = pathlib.Path(os.getenv("CHECKPOINTS_PATH", "/workspace/seamless_communication/demo/expressive/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


from pydub import AudioSegment

def adjust_audio_duration(input_audio_path, output_audio_path):
    input_audio = AudioSegment.from_file(input_audio_path)
    output_audio = AudioSegment.from_file(output_audio_path)

    input_duration = len(input_audio)
    output_duration = len(output_audio)

    # Calcul de la différence de durée
    duration_diff = input_duration - output_duration

    # Ajout de silence à la fin si l'audio de sortie est plus court
    if duration_diff > 0:
        print("Duration diff : ",duration_diff)
        silence = AudioSegment.silent(duration=duration_diff)
        output_audio += silence

        # Enregistrer l'audio ajusté
        output_audio.export(output_audio_path, format='wav')

    return output_audio_path




import yt_dlp
def dowloadYoutubeAudio(url):
    print("Téléchargement de l'audio YouTube en cours...")
    ydl_opts = {
          'format': 'm4a/bestaudio/best',
        'outtmpl': os.getcwd() + "/audio",  # Mise à jour du chemin de sortie
        'postprocessors': [{
            'key': 'FFmpegExtractAudio',
            'preferredcodec': 'wav',  # Utilisation du format WAV
        }]
    }
    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
        error_code = ydl.download([url])

    if error_code == 0:
        print("Sauvegarde du fichier audio...")
        print("download_finished : ", os.getcwd() + "/audio.wav")
    else:
        print("error : Échec du téléchargement...")

    return os.getcwd() + "/audio.wav"


def split_audio(input_audio_path):
    print("Start Split Audio")
    audio = AudioSegment.from_file(input_audio_path)
    silence_thresh = -20  # Seuil de silence
    min_silence_len = 300  # Durée minimale de silence en ms

    chunks = []
    current_chunk = AudioSegment.silent(duration=0)
    for ms in range(0, len(audio), 10):  # Incrément de 10 ms
        segment = audio[ms:ms + 10]
        current_chunk += segment

        if len(current_chunk) >= 8000:  # Si la durée actuelle dépasse 8 secondes
            # Vérifier s'il y a un silence
            if silence.detect_silence(current_chunk[-min_silence_len:], min_silence_len=min_silence_len, silence_thresh=silence_thresh):
                # Couper au silence
                print("Silence détecté, découpage du segment")
                chunks.append(current_chunk)
                current_chunk = AudioSegment.silent(duration=0)

        if len(current_chunk) >= 8900:  # Si la durée dépasse 9,89 secondes
            print("Durée maximale atteinte, découpage du segment")
            chunks.append(current_chunk)
            current_chunk = AudioSegment.silent(duration=0)

    # Ajouter le dernier segment s'il n'est pas vide
    if len(current_chunk) > 0:
        chunks.append(current_chunk)

    print('Nombre de segments valides:', len(chunks))
    return chunks




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






import torchaudio

AUDIO_SAMPLE_RATE = 16000  # Taux d'échantillonnage standard

def preprocess_audio(input_audio_path: str):
    print("preprocess_audio start")
    print("Audio Path :", input_audio_path)
    audio_segments = split_audio(input_audio_path)
    temp_folder = os.path.join(os.getcwd(), "path_to_temp_folder")
    os.makedirs(temp_folder, exist_ok=True)
    segment_paths = []

    for i, segment in enumerate(audio_segments):
        segment_path = os.path.join(temp_folder, f"segment_{i}.wav")
        segment_audio = segment.get_array_of_samples()
        segment_tensor = torch.tensor(segment_audio).unsqueeze(0).float()
        
        # Rééchantillonnage
        segment_tensor = torchaudio.functional.resample(segment_tensor, orig_freq=segment.frame_rate, new_freq=AUDIO_SAMPLE_RATE)

        torchaudio.save(segment_path, segment_tensor, sample_rate=AUDIO_SAMPLE_RATE)
        segment_paths.append(segment_path)
        print("path for :", segment_path)

    return segment_paths



import os
import torchaudio

# Constante pour le taux d'échantillonnage
AUDIO_SAMPLE_RATE = 16000

def preprocess_audio22(input_audio_path: str):
    print("preprocess_audio start")
    print("Audio Path :", input_audio_path)

    # Appeler split_audio et obtenir les segments
    audio_segments = split_audio(input_audio_path)

    # Créer un dossier temporaire pour stocker les segments
    temp_folder = os.path.join(os.getcwd(), "path_to_temp_folder")
    os.makedirs(temp_folder, exist_ok=True)

    segment_paths = []
    for i, segment in enumerate(audio_segments):
        # Exporter chaque segment dans un fichier temporaire
        temp_segment_path = os.path.join(temp_folder, f"temp_segment_{i}.wav")
        segment.export(temp_segment_path, format="wav")

        # Charger et traiter le segment audio
        arr, org_sr = torchaudio.load(temp_segment_path)
        new_arr = torchaudio.functional.resample(arr, orig_freq=org_sr, new_freq=AUDIO_SAMPLE_RATE)

        # Enregistrer le segment traité
        segment_path = os.path.join(temp_folder, f"segment_{i}.wav")
        torchaudio.save(segment_path, new_arr, sample_rate=AUDIO_SAMPLE_RATE)

        # Ajouter le chemin du segment traité à la liste
        segment_paths.append(segment_path)
        print("Path for :", segment_path)

    return segment_paths


def preprocess_audio222(input_audio_path: str):
    # Appeler split_audio et obtenir les segments
    print("preprocess_audio start")
    print("Audio Path :",input_audio_path)
    audio_segments = split_audio(input_audio_path)
    temp_folder = os.getcwd()+"/path_to_temp_folder"
    os.makedirs(temp_folder, exist_ok=True)
    segment_paths = []
    for i, segment in enumerate(audio_segments):
        segment_path = os.path.join(temp_folder, f"segment_{i}.wav")
        segment.export(segment_path, format="wav")
        segment_paths.append(segment_path)
        print("path for : ",segment_path)
    
    return segment_paths




def process_segment(segment_path, source_language_code, target_language_code):
    # preprocess_audio(segment_path) - cette ligne peut ne pas être nécessaire si le segment est déjà prétraité

    with pathlib.Path(segment_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)

    # Transcription pour mintox
    source_sentences, _ = m4t_translator.predict(
        input=example["fbank"],
        task_str="S2TT",
        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,
    )
    speech_output = pretssel_generator.predict(
        unit_output.units,
        tgt_lang=target_language_code,
        prosody_encoder_input=prosody_encoder_input,
    )

    # Chemin pour enregistrer l'audio du segment
    segment_output_audio_path = os.path.join(os.getcwd(), "result", f"segment_audio_{os.path.basename(segment_path)}")
    os.makedirs(os.path.dirname(segment_output_audio_path), exist_ok=True)
    
    # Enregistrer l'audio du segment
    torchaudio.save(
        segment_output_audio_path,
        speech_output.audio_wavs[0][0].to(torch.float32).cpu(),
        sample_rate=speech_output.sample_rate,
    )
    segment_output_audio_path = adjust_audio_duration(segment_path, segment_output_audio_path)

    
    text_out = remove_prosody_tokens_from_text(str(text_output[0]))
    print("Audio ici : ",segment_output_audio_path)
    return segment_output_audio_path, text_out


#---------------------------_#


from typing import Tuple

def run2(
    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










#---------------------------------------------------------_#
#----------------------------------------------------------#









#----------------------------------------------__#------










#-----------------------#


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]
    
    segment_paths = preprocess_audio22(input_audio_path)
    print("preprocess_audio end")
    final_text = ""
    final_audio = AudioSegment.silent(duration=0)


    for segment_path in segment_paths:
        segment_audio_path, segment_text = process_segment(segment_path, source_language_code, target_language_code)
        final_text += segment_text + " "
        segment_audio = AudioSegment.from_file(segment_audio_path)
        final_audio += segment_audio

    output_audio_path = os.path.join(os.getcwd(), "result", "audio.wav")
    os.makedirs(os.path.dirname(output_audio_path), exist_ok=True)
    final_audio.export(output_audio_path, format="wav")

    text_out = remove_prosody_tokens_from_text(final_text.strip())
    
    return output_audio_path, text_out





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


from flask import Flask, request, jsonify
import torch
import torchaudio

app = Flask(__name__)
# Fonction run adaptée pour Flask
@app.route('/translate', methods=['POST'])
def translate():
    # Récupérer les données de la requête
    data = request.json
    input_audio_path = data['input_audio_path']
    source_language = data['source_language']
    target_language = data['target_language']

    # Exécution de la fonction de traduction
    output_audio_path, output_text = run(input_audio_path, source_language, target_language)

    # Retourner la réponse
    return jsonify({
        'output_audio_path': output_audio_path,
        'output_text': output_text
    })


import os

url = "https://youtu.be/qb_tHWGJOp8?si=10qB2JApy0q3XY76"
input_audio_path = dowloadYoutubeAudio(url)

#input_audio_path = os.getcwd()+"/au1min_Vocals_finale.wav"
source_language = "French"
target_language = "English"
print("Audio à traiter : ",input_audio_path)
output_audio_path, output_text = run(input_audio_path, source_language, target_language)

print("output_audio_path : ",output_audio_path)