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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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mms_transcribe = gr.Interface( |
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fn=transcribe, |
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inputs=[ |
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gr.Audio(), |
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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_identify = gr.Interface( |
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fn=identify, |
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inputs=[ |
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gr.Audio(), |
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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</p>" |
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) |
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gr.HTML( |
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"""<center>Text-to-Speech, Speech-to-Text, and Language Recognition for 1,100+ languages.</center>""" |
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) |
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tabbed_interface.render() |
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if __name__ == "__main__": |
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demo.queue() |
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demo.launch() |
