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import torch |
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import gradio as gr |
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import pytube as pt |
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from transformers import pipeline |
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from huggingface_hub import model_info |
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MODEL_NAME = "thomas0104/whisper-large-v2-nan-tw-only-char" |
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lang = "chinese" |
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device = 0 if torch.cuda.is_available() else "cpu" |
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pipe = pipeline( |
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task="automatic-speech-recognition", |
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model=MODEL_NAME, |
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chunk_length_s=30, |
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device=device, |
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) |
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pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe") |
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def transcribe(microphone, file_upload): |
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warn_output = "" |
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if (microphone is not None) and (file_upload is not None): |
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warn_output = ( |
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"WARNING: You've uploaded an audio file and used the microphone. " |
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"The recorded file from the microphone will be used and the uploaded audio will be discarded.\n" |
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) |
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elif (microphone is None) and (file_upload is None): |
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return "ERROR: You have to either use the microphone or upload an audio file" |
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file = microphone if microphone is not None else file_upload |
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text = pipe(file)["text"] |
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return warn_output + text |
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def _return_yt_html_embed(yt_url): |
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video_id = yt_url.split("?v=")[-1] |
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HTML_str = ( |
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f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>' |
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" </center>" |
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) |
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return HTML_str |
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def yt_transcribe(yt_url): |
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yt = pt.YouTube(yt_url) |
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html_embed_str = _return_yt_html_embed(yt_url) |
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stream = yt.streams.filter(only_audio=True)[0] |
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stream.download(filename="audio.mp3") |
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text = pipe("audio.mp3")["text"] |
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return html_embed_str, text |
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demo = gr.Blocks() |
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mf_transcribe = gr.Interface( |
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fn=transcribe, |
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inputs=[ |
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gr.inputs.Audio(source="microphone", type="filepath", optional=True), |
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gr.inputs.Audio(source="upload", type="filepath", optional=True), |
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], |
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outputs="text", |
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layout="horizontal", |
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theme="huggingface", |
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title="Whisper Demo: Transcribe Audio", |
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description=( |
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"Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the the fine-tuned" |
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f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files" |
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" of arbitrary length." |
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), |
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allow_flagging="never", |
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) |
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with demo: |
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gr.TabbedInterface([mf_transcribe], ["Transcribe Audio"]) |
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demo.launch(enable_queue=True,share=True) |
