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| 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 is not None) and (file_upload is not None): | |
| 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" | |
| ) | |
| elif (microphone is None) and (file_upload is None): | |
| return "ERROR: You have to either use the microphone or upload an audio file" | |
| file = microphone if microphone is not None else file_upload | |
| 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): | |
| yt = pt.YouTube(yt_url) | |
| html_embed_str = _return_yt_html_embed(yt_url) | |
| stream = yt.streams.filter(only_audio=True)[0] | |
| stream.download(filename="audio.mp3") | |
| text = pipe("audio.mp3", generate_kwargs={"language":"<|th|>", "task":"transcribe"}, batch_size=16)["text"] | |
| return html_embed_str, text | |
| with gr.Blocks(theme=gr.themes.HuggingFace()) as demo: | |
| gr.Markdown(f"# Whisper Demo Thai 🇹🇭") | |
| 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(source="microphone", type="filepath", label="Microphone Input") | |
| audio_file = gr.Audio(source="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]) | |
| demo.launch(enable_queue=True) |