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from transformers import pipeline |
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import gradio as gr |
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import torch |
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from examples import * |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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asr = pipeline( |
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"automatic-speech-recognition", |
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model="CLiC-UB/Casper", |
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chunk_length_s=30, |
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device=device, |
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) |
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def transcribe_audio(file=None, mic=None): |
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if mic is not None: |
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audio = mic |
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elif file is not None: |
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audio = file |
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else: |
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return "You must either provide a mic recording or a file" |
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transcription = asr(audio)["text"] |
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return transcription |
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with gr.Blocks() as demo: |
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gr.Markdown("<center><h1>CASPER</h1> " |
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"<h2>Catalan Automatic Speech Recognition using Fine-Tuned Whisper</h2></center>") |
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with gr.Row(): |
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with gr.Column(): |
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audio_from_microphone = gr.Audio(source="microphone", label="Mic", type="filepath") |
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audio_from_file = gr.Audio(source="upload", label="File", type="filepath") |
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with gr.Row(): |
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with gr.Column(scale=2): |
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asr_btn = gr.Button("Transcribe!") |
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with gr.Column(scale=0): |
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cln_btn = gr.ClearButton(value='Clear', components=[audio_from_microphone, audio_from_file]) |
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with gr.Column(): |
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output_text = gr.Textbox(label="Generated Transcription") |
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del_text = gr.ClearButton(value='Delete Text', components=output_text) |
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gr.Markdown("<sub>NOTE: This model does not generate punctuation and casing</sub>") |
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asr_btn.click(fn=transcribe_audio, |
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inputs=[audio_from_file, audio_from_microphone], |
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outputs=output_text) |
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with gr.Row(): |
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with gr.Column(): |
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gr.Markdown("### Audio Examples") |
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gr.Examples(examples=infer_from_audio_examples, |
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label="From Catalan Google TTS dataset", |
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inputs=[audio_from_file, audio_from_microphone], |
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outputs=output_text, |
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fn=transcribe_audio, |
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cache_examples=True, ) |
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gr.Markdown("### More Details") |
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gr.Markdown("The model used is a small version of the Whisper architecture. " |
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"Please, find more details about it in this [link](https://huggingface.co/openai/whisper-small) <br>" |
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"Whisper has been fine-tuned using the catalan CommonVoice v.11 and the ParlamentParla datasets. " |
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"More information about results and evaluation can be found in " |
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"[here](https://huggingface.co/MaximilianChen/Casper)") |
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demo.launch() |
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