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Running
on
T4
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'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>' | |
" </center>" | |
) | |
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.Markdown("# Thonburian Whisper Demo ๐น๐ญ") | |
gr.Image(value="thonburian-whisper-logo.png", show_label=False, container=False, width=400) | |
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 ๐ค 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 ๐ค 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() |