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import gradio as gr |
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import librosa |
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from asr import transcribe, ASR_EXAMPLES, ASR_LANGUAGES, ASR_NOTE |
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from tts import synthesize, TTS_EXAMPLES, TTS_LANGUAGES |
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from lid import identify, LID_EXAMPLES |
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demo = gr.Blocks() |
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mms_select_source_trans = gr.Radio( |
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["Record from Mic", "Upload audio"], |
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label="Audio input", |
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value="Record from Mic", |
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) |
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mms_mic_source_trans = gr.Audio(source="microphone", type="filepath", label="Use mic") |
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mms_upload_source_trans = gr.Audio( |
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source="upload", type="filepath", label="Upload file", visible=False |
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) |
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mms_transcribe = gr.Interface( |
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fn=transcribe, |
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inputs=[ |
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mms_select_source_trans, |
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mms_mic_source_trans, |
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mms_upload_source_trans, |
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gr.Dropdown( |
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[f"{k} ({v})" for k, v in ASR_LANGUAGES.items()], |
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label="Language", |
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value="eng English", |
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), |
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], |
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outputs="text", |
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examples=ASR_EXAMPLES, |
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title="Speech-to-text", |
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description=( |
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"Transcribe audio from a microphone or input file in your desired language." |
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), |
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article=ASR_NOTE, |
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allow_flagging="never", |
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) |
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mms_synthesize = gr.Interface( |
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fn=synthesize, |
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inputs=[ |
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gr.Text(label="Input text"), |
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gr.Dropdown( |
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[f"{k} ({v})" for k, v in TTS_LANGUAGES.items()], |
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label="Language", |
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value="eng English", |
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), |
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gr.Slider(minimum=0.1, maximum=4.0, value=1.0, step=0.1, label="Speed"), |
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], |
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outputs=[ |
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gr.Audio(label="Generated Audio", type="numpy"), |
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gr.Text(label="Filtered text after removing OOVs"), |
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], |
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examples=TTS_EXAMPLES, |
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title="Text-to-speech", |
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description=("Generate audio in your desired language from input text."), |
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allow_flagging="never", |
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) |
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mms_select_source_iden = gr.Radio( |
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["Record from Mic", "Upload audio"], |
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label="Audio input", |
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value="Record from Mic", |
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) |
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mms_mic_source_iden = gr.Audio(source="microphone", type="filepath", label="Use mic") |
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mms_upload_source_iden = gr.Audio( |
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source="upload", type="filepath", label="Upload file", visible=False |
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) |
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mms_identify = gr.Interface( |
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fn=identify, |
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inputs=[ |
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mms_select_source_iden, |
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mms_mic_source_iden, |
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mms_upload_source_iden, |
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], |
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outputs=gr.Label(num_top_classes=10), |
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examples=LID_EXAMPLES, |
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title="Language Identification", |
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description=("Identity the language of input audio."), |
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allow_flagging="never", |
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) |
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tabbed_interface = gr.TabbedInterface( |
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[mms_transcribe, mms_synthesize, mms_identify], |
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["Speech-to-text", "Text-to-speech", "Language Identification"], |
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) |
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with gr.Blocks() as demo: |
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gr.Markdown( |
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"<p align='center' style='font-size: 20px;'>MMS: Scaling Speech Technology to 1000+ languages demo. See our <a href='https://ai.facebook.com/blog/multilingual-model-speech-recognition/'>blog post</a> and <a href='https://arxiv.org/abs/2305.13516'>paper</a>.</p>" |
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) |
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gr.HTML( |
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"""<center>Click on the appropriate tab to explore Speech-to-text (ASR), Text-to-speech (TTS) and Language identification (LID) demos. </center>""" |
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) |
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gr.HTML( |
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"""<center><a href="https://huggingface.co/spaces/facebook/MMS?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank"><img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> for more control and no queue.</center>""" |
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) |
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tabbed_interface.render() |
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mms_select_source_trans.change( |
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lambda x: [ |
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gr.update(visible=True if x == "Record from Mic" else False), |
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gr.update(visible=True if x == "Upload audio" else False), |
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], |
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inputs=[mms_select_source_trans], |
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outputs=[mms_mic_source_trans, mms_upload_source_trans], |
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queue=False, |
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) |
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mms_select_source_iden.change( |
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lambda x: [ |
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gr.update(visible=True if x == "Record from Mic" else False), |
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gr.update(visible=True if x == "Upload audio" else False), |
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], |
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inputs=[mms_select_source_iden], |
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outputs=[mms_mic_source_iden, mms_upload_source_iden], |
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queue=False, |
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) |
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gr.HTML( |
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""" |
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<div class="footer" style="text-align:center"> |
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<p> |
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Model by <a href="https://ai.facebook.com" style="text-decoration: underline;" target="_blank">Meta AI</a> - Gradio Demo by 🤗 Hugging Face |
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</p> |
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</div> |
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""" |
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) |
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demo.queue(concurrency_count=3) |
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demo.launch() |
