import os os.system("pip uninstall -y gradio") os.system("pip install gradio==3.36.1") import torch import time import gradio as gr import yt_dlp as youtube_dl from transformers import pipeline from transformers.pipelines.audio_utils import ffmpeg_read import tempfile MODEL_NAME = "openai/whisper-large-v3" 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 chunks_to_srt(chunks): srt_format = "" for i, chunk in enumerate(chunks, 1): start_time, end_time = chunk['timestamp'] start_time_hms = "{:02}:{:02}:{:02},{:03}".format(int(start_time // 3600), int((start_time % 3600) // 60), int(start_time % 60), int((start_time % 1) * 1000)) end_time_hms = "{:02}:{:02}:{:02},{:03}".format(int(end_time // 3600), int((end_time % 3600) // 60), int(end_time % 60), int((end_time % 1) * 1000)) srt_format += f"{i}\n{start_time_hms} --> {end_time_hms}\n{chunk['text']}\n\n" return srt_format def transcribe(inputs, task, return_timestamps, language): if inputs is None: raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.") # Map the language names to their corresponding codes language_codes = {"English": "en", "Uzbek": "uz"} language_code = language_codes.get(language, "uz") # Default to "en" if the language is not found result = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task, "language": f"<|{language_code}|>"}, return_timestamps=return_timestamps) if return_timestamps: return chunks_to_srt(result['chunks']) else: return result['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, return_timestamps, language, 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} # Map the language names to their corresponding codes language_codes = {"English": "en", "Uzbek": "uz"} language_code = language_codes.get(language, "uz") # Default to "en" if the language is not found result = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task, "language": f"<|{language_code}|>"}, return_timestamps=return_timestamps) if return_timestamps: return html_embed_str, chunks_to_srt(result['chunks']) else: return html_embed_str, result['text'] demo = gr.Blocks() print((gr.__version__), 'gradio version check') mf_transcribe = gr.Interface( fn=transcribe, inputs=[ gr.inputs.Audio(source="microphone", type="filepath", optional=True), gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"), gr.inputs.Checkbox(label="Return timestamps"), gr.inputs.Dropdown(choices=["English", "Uzbek"], label="Language"), ], outputs="text", layout="horizontal", theme="huggingface", title="Whisper Large v3 Uzbek: Transcribe Audio", description=( "\n\n" "
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" ), allow_flagging="never", ) file_transcribe = gr.Interface( fn=transcribe, inputs=[ gr.inputs.Audio(source="upload", type="filepath", optional=True, label="Audio file"), gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"), gr.inputs.Checkbox(label="Return timestamps"), gr.inputs.Dropdown(choices=["English", "Uzbek"], label="Language"), ], outputs="text", layout="horizontal", theme="huggingface", title="Whisper Large v3 Uzbek: Transcribe Audio File", description=( "\n\n" "
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" ), allow_flagging="never", ) yt_transcribe = gr.Interface( fn=yt_transcribe, inputs=[ gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"), gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"), gr.inputs.Checkbox(label="Return timestamps"), gr.inputs.Dropdown(choices=["English", "Uzbek"], label="Language"), ], outputs=["html", "text"], layout="horizontal", theme="huggingface", title="Whisper Large v3 Uzbek: Transcribe YouTube", description=( "\n\n" "
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" ), allow_flagging="never", ) with demo: gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"]) demo.launch(enable_queue=True)