import os import yt_dlp import torch import gradio as gr import pytube as pt from transformers import pipeline from huggingface_hub import model_info MODEL_NAME = "biodatlab/whisper-th-medium-combined" # this always needs to stay in line 8 :D sorry for the hackiness lang = "th" device = 0 if torch.cuda.is_available() else "cpu" pipe = pipeline( task="automatic-speech-recognition", model=MODEL_NAME, chunk_length_s=30, device=device, ) pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe") def transcribe(microphone, file_upload): warn_output = "" if microphone and file_upload: warn_output = ( "WARNING: You've uploaded an audio file and used the microphone. " "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n" ) file = microphone elif microphone: file = microphone elif file_upload: file = file_upload else: return "ERROR: You have to either use the microphone or upload an audio file" text = pipe(file, generate_kwargs={"language":"<|th|>", "task":"transcribe"}, batch_size=16)["text"] return warn_output + text def _return_yt_html_embed(yt_url): video_id = yt_url.split("?v=")[-1] HTML_str = ( f'
' "
" ) return HTML_str def yt_transcribe(yt_url): try: ydl_opts = { 'format': 'bestaudio/best', 'postprocessors': [{ 'key': 'FFmpegExtractAudio', 'preferredcodec': 'mp3', 'preferredquality': '192', }], 'outtmpl': 'audio.%(ext)s', } with yt_dlp.YoutubeDL(ydl_opts) as ydl: info = ydl.extract_info(yt_url, download=True) video_id = info['id'] html_embed_str = _return_yt_html_embed(video_id) text = pipe("audio.mp3", generate_kwargs={"language":"<|th|>", "task":"transcribe"}, batch_size=16)["text"] # Clean up the downloaded file os.remove("audio.mp3") return html_embed_str, text except Exception as e: return f"Error: {str(e)}", "An error occurred while processing the YouTube video." with gr.Blocks() as demo: gr.HTML( """
Thonburian Whisper Logo

Thonburian Whisper Demo 🇹🇭

""" ) with gr.Tab("Transcribe Audio"): gr.Markdown( f"Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the fine-tuned" f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files" f" of arbitrary length." ) with gr.Row(): with gr.Column(): audio_mic = gr.Audio(sources=["microphone"], type="filepath", label="Microphone Input") audio_file = gr.Audio(sources=["upload"], type="filepath", label="Audio File Upload") with gr.Column(): text_output = gr.Textbox(label="Transcription Output") transcribe_btn = gr.Button("Transcribe") transcribe_btn.click(fn=transcribe, inputs=[audio_mic, audio_file], outputs=text_output) with gr.Tab("Transcribe YouTube"): gr.Markdown( f"Transcribe long-form YouTube videos with the click of a button! Demo uses the fine-tuned checkpoint:" f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files of" f" arbitrary length." ) with gr.Row(): with gr.Column(): yt_url_input = gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL") with gr.Column(): yt_html_output = gr.HTML(label="Video") yt_text_output = gr.Textbox(label="Transcription Output") yt_transcribe_btn = gr.Button("Transcribe YouTube Video") yt_transcribe_btn.click(fn=yt_transcribe, inputs=yt_url_input, outputs=[yt_html_output, yt_text_output]) if __name__ == "__main__": demo.queue().launch()