import torch import gradio as gr import yt_dlp as youtube_dl from transformers import pipeline from transformers.pipelines.audio_utils import ffmpeg_read from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline from flores200_codes import flores_codes from gradio.components import Audio, Dropdown, Radio, Textbox import tempfile import os MODEL_NAME = "openai/whisper-large-v2" BATCH_SIZE = 8 FILE_LIMIT_MB = 1000 YT_LENGTH_LIMIT_S = 3600 # limit to 1 hour YouTube files device = 0 if torch.cuda.is_available() else "cpu" pipe = pipeline( task="automatic-speech-recognition", model=MODEL_NAME, chunk_length_s=30, device=device, ) def load_models(): # build model and tokenizer model_name_dict = { 'nllb-distilled-600M': 'facebook/nllb-200-distilled-600M', #'nllb-distilled-1.3B': 'facebook/nllb-200-distilled-1.3B', #'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', } model_dict = {} for call_name, real_name in model_name_dict.items(): print('\tLoading model: %s' % call_name) model = AutoModelForSeq2SeqLM.from_pretrained(real_name) tokenizer = AutoTokenizer.from_pretrained(real_name) model_dict[call_name+'_model'] = model model_dict[call_name+'_tokenizer'] = tokenizer return model_dict def translation(source, target, text): try: print("Début de la traduction") if len(model_dict) == 2: model_name = 'nllb-distilled-1.3B' start_time = time.time() source = flores_codes[source] target = flores_codes[target] model = model_dict[model_name + '_model'] tokenizer = model_dict[model_name + '_tokenizer'] translator = pipeline('translation', model=model, tokenizer=tokenizer, src_lang=source, tgt_lang=target) output = translator(text, max_length=400) end_time = time.time() output = output[0]['translation_text'] result = {'inference_time': end_time - start_time, 'source': source, 'target': target, 'result': output} print("Fin de la transcription") except Exception as e: print(f"Erreur lors de la transcription : {e}") return result def transcribe(inputs, task, source, target): if inputs is None: raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.") try: print("Début de la transcription") text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"] translated_text = translation(source, target, text) print("Fin de la transcription") except Exception as e: print(f"Erreur lors de la transcription : {e}") return text, translated_text def _return_yt_html_embed(yt_url): video_id = yt_url.split("?v=")[-1] HTML_str = ( f'
' "
" ) 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) 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, pipe.feature_extractor.sampling_rate) inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate} text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"] translated_text = translation(source, target, text) return html_embed_str, text, translated_text global model_dict model_dict = load_models() demo = gr.Blocks() lang_codes = list(flores_codes.keys()) mf_transcribe = gr.Interface( fn=transcribe, inputs=[ Audio(source="microphone", type="filepath"), Radio(["transcribe", "translate"], label="Task"), Dropdown(lang_codes, default='English', label='Source'), Dropdown(lang_codes, default='French', label='Target'), ], outputs=[Textbox(label="Transcribed Text"), Textbox(label="Translated Text")], layout="horizontal", theme="huggingface", title="Whisper Large V2: Transcribe Audio", description=( "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the" f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files" " of arbitrary length." ), allow_flagging="never", ) file_transcribe = gr.Interface( fn=transcribe, inputs=[ Audio(source="upload", type="filepath", label="Audio file"), Radio(["transcribe", "translate"], label="Task"), Dropdown(lang_codes, default='English', label='Source'), Dropdown(lang_codes, default='French', label='Target'), ], outputs=[Textbox(label="Transcribed Text"), Textbox(label="Translated Text")], layout="horizontal", theme="huggingface", title="Whisper Large V2: Transcribe Audio", description=( "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the" f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files" " of arbitrary length." ), allow_flagging="never", ) yt_transcribe = gr.Interface( fn=yt_transcribe, inputs=[ Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"), Radio(["transcribe", "translate"], label="Task"), Dropdown(lang_codes, default='English', label='Source'), Dropdown(lang_codes, default='French', label='Target'), ], outputs=[Textbox(label="html"), Textbox(label="Transcribed Text"), Textbox(label="Translated Text")], layout="horizontal", theme="huggingface", title="Whisper Large V2: Transcribe YouTube", description=( "Transcribe long-form YouTube videos with the click of a button! Demo uses the checkpoint" f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe video files of" " arbitrary length." ), allow_flagging="never", ) with demo: gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"]) demo.launch().queue()