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import torch
import gradio as gr
from faster_whisper import WhisperModel
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from pydub import AudioSegment
import yt_dlp as youtube_dl
import tempfile
from transformers.pipelines.audio_utils import ffmpeg_read
from gradio.components import Audio, Dropdown, Radio, Textbox
import os
import numpy as np
import soundfile as sf
os.environ["TOKENIZERS_PARALLELISM"] = "false"
# Paramètres
FILE_LIMIT_MB = 1000
YT_LENGTH_LIMIT_S = 3600 # Limite de 1 heure pour les vidéos YouTube
# Charger les codes de langue
from flores200_codes import flores_codes
# Fonction pour déterminer le device
def set_device():
return torch.device("cuda" if torch.cuda.is_available() else "cpu")
device = set_device()
# Charger les modèles une seule fois
model_dict = {}
def load_models():
global model_dict
if not model_dict:
model_name_dict = {
#'nllb-distilled-1.3B': 'facebook/nllb-200-distilled-1.3B',
'nllb-distilled-600M': 'facebook/nllb-200-distilled-600M',
#'nllb-1.3B': 'facebook/nllb-200-1.3B',
#'nllb-distilled-1.3B': 'facebook/nllb-200-distilled-1.3B',
#'nllb-3.3B': 'facebook/nllb-200-3.3B',
# 'nllb-distilled-600M': 'facebook/nllb-200-distilled-600M',
}
for call_name, real_name in model_name_dict.items():
model = AutoModelForSeq2SeqLM.from_pretrained(real_name)
tokenizer = AutoTokenizer.from_pretrained(real_name)
model_dict[call_name+'_model'] = model
model_dict[call_name+'_tokenizer'] = tokenizer
load_models()
model_size = "large-v2"
model = WhisperModel(model_size)
# Fonction pour la transcription
def transcribe_audio(audio_file):
# model_size = "large-v2"
# model = WhisperModel(model_size)
# model = WhisperModel(model_size, device=device, compute_type="int8")
global model
segments, _ = model.transcribe(audio_file, beam_size=1)
transcriptions = [("[%.2fs -> %.2fs]" % (seg.start, seg.end), seg.text) for seg in segments]
return transcriptions
# Fonction pour la traduction
def traduction(text, source_lang, target_lang):
# Vérifier si les codes de langue sont dans flores_codes
if source_lang not in flores_codes or target_lang not in flores_codes:
print(f"Code de langue non trouvé : {source_lang} ou {target_lang}")
return ""
src_code = flores_codes[source_lang]
tgt_code = flores_codes[target_lang]
model_name = "nllb-distilled-600M"
model = model_dict[model_name + "_model"]
tokenizer = model_dict[model_name + "_tokenizer"]
translator = pipeline("translation", model=model, tokenizer=tokenizer)
return translator(text, src_lang=src_code, tgt_lang=tgt_code)[0]["translation_text"]
# Fonction principale
def full_transcription_and_translation(audio_input, source_lang, target_lang):
# Si audio_input est une URL
if isinstance(audio_input, str) and audio_input.startswith("http"):
audio_file = download_yt_audio(audio_input)
# Si audio_input est un dictionnaire contenant des données audio
elif isinstance(audio_input, dict) and "array" in audio_input and "sampling_rate" in audio_input:
audio_array = audio_input["array"]
sampling_rate = audio_input["sampling_rate"]
# Écrire le tableau NumPy dans un fichier temporaire WAV
with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as f:
sf.write(f, audio_array, sampling_rate)
audio_file = f.name
else:
# Supposons que c'est un chemin de fichier
audio_file = audio_input
transcriptions = transcribe_audio(audio_file)
translations = [(timestamp, traduction(text, source_lang, target_lang)) for timestamp, text in transcriptions]
# Supprimez le fichier temporaire s'il a été créé
if isinstance(audio_input, dict):
os.remove(audio_file)
return transcriptions, translations
# Téléchargement audio YouTube
"""def download_yt_audio(yt_url):
with tempfile.NamedTemporaryFile(suffix='.mp3') as f:
ydl_opts = {
'format': 'bestaudio/best',
'outtmpl': f.name,
'postprocessors': [{
'key': 'FFmpegExtractAudio',
'preferredcodec': 'mp3',
'preferredquality': '192',
}],
}
with youtube_dl.YoutubeDL(ydl_opts) as ydl:
ydl.download([yt_url])
return f.name"""
lang_codes = list(flores_codes.keys())
# Interface Gradio
def gradio_interface(audio_file, source_lang, target_lang):
if audio_file.startswith("http"):
audio_file = download_yt_audio(audio_file)
transcriptions, translations = full_transcription_and_translation(audio_file, source_lang, target_lang)
transcribed_text = '\n'.join([f"{timestamp}: {text}" for timestamp, text in transcriptions])
translated_text = '\n'.join([f"{timestamp}: {text}" for timestamp, text in translations])
return transcribed_text, translated_text
def _return_yt_html_embed(yt_url):
video_id = yt_url.split("?v=")[-1]
HTML_str = (
f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
" </center>"
)
return HTML_str
def download_yt_audio(yt_url, filename):
info_loader = youtube_dl.YoutubeDL()
try:
info = info_loader.extract_info(yt_url, download=False)
except youtube_dl.utils.DownloadError as err:
raise gr.Error(str(err))
file_length = info["duration_string"]
file_h_m_s = file_length.split(":")
file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
if len(file_h_m_s) == 1:
file_h_m_s.insert(0, 0)
if len(file_h_m_s) == 2:
file_h_m_s.insert(0, 0)
file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]
if file_length_s > YT_LENGTH_LIMIT_S:
yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S))
file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s))
raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.")
ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
with youtube_dl.YoutubeDL(ydl_opts) as ydl:
try:
ydl.download([yt_url])
except youtube_dl.utils.ExtractorError as err:
raise gr.Error(str(err))
def yt_transcribe(yt_url, task, max_filesize=75.0):
html_embed_str = _return_yt_html_embed(yt_url)
global model # S'assurer que le modèle est accessible
with tempfile.TemporaryDirectory() as tmpdirname:
filepath = os.path.join(tmpdirname, "video.mp4")
download_yt_audio(yt_url, filepath)
with open(filepath, "rb") as f:
inputs = f.read()
inputs = ffmpeg_read(inputs, model.feature_extractor.sampling_rate)
inputs = {"array": inputs, "sampling_rate": model.feature_extractor.sampling_rate}
transcriptions, translations = full_transcription_and_translation(inputs, source_lang, target_lang)
transcribed_text = '\n'.join([f"{timestamp}: {text}" for timestamp, text in transcriptions])
translated_text = '\n'.join([f"{timestamp}: {text}" for timestamp, text in translations])
return html_embed_str, transcribed_text, translated_text
# Interfaces
demo = gr.Blocks()
with demo:
with gr.Tab("Microphone"):
gr.Interface(
fn=gradio_interface,
inputs=[
gr.Audio(sources=["microphone"], type="filepath"),
gr.Dropdown(lang_codes, value='French', label='Source Language'),
gr.Dropdown(lang_codes, value='English', label='Target Language')],
outputs=[gr.Textbox(label="Transcribed Text"), gr.Textbox(label="Translated Text")]
)
with gr.Tab("Audio file"):
gr.Interface(
fn=gradio_interface,
inputs=[
gr.Audio(type="filepath", label="Audio file"),
gr.Dropdown(lang_codes, value='French', label='Source Language'),
gr.Dropdown(lang_codes, value='English', label='Target Language')],
outputs=[gr.Textbox(label="Transcribed Text"), gr.Textbox(label="Translated Text")]
)
with gr.Tab("YouTube"):
gr.Interface(
fn=yt_transcribe,
inputs=[
gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
gr.Dropdown(lang_codes, value='French', label='Source Language'),
gr.Dropdown(lang_codes, value='English', label='Target Language')
],
outputs=["html", gr.Textbox(label="Transcribed Text"), gr.Textbox(label="Translated Text")]
)
#with demo:
#gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
demo.launch() |