import gradio as gr import librosa from asr import transcribe, ASR_EXAMPLES, ASR_LANGUAGES, ASR_NOTE from tts import synthesize, TTS_EXAMPLES, TTS_LANGUAGES from lid import identify, LID_EXAMPLES mms_transcribe = gr.Interface( fn=transcribe, inputs=[ gr.Audio(), gr.Dropdown( [f"{k} ({v})" for k, v in ASR_LANGUAGES.items()], label="Language", value="eng English", ), # gr.Checkbox(label="Use Language Model (if available)", default=True), ], outputs="text", examples=ASR_EXAMPLES, title="Speech-to-text", description=( "Transcribe audio from a microphone or input file in your desired language." ), article=ASR_NOTE, allow_flagging="never", ) mms_synthesize = gr.Interface( fn=synthesize, inputs=[ gr.Text(label="Input text"), gr.Dropdown( [f"{k} ({v})" for k, v in TTS_LANGUAGES.items()], label="Language", value="eng English", ), gr.Slider(minimum=0.1, maximum=4.0, value=1.0, step=0.1, label="Speed"), ], outputs=[ gr.Audio(label="Generated Audio", type="numpy"), gr.Text(label="Filtered text after removing OOVs"), ], examples=TTS_EXAMPLES, title="Text-to-speech", description=("Generate audio in your desired language from input text."), allow_flagging="never", ) mms_identify = gr.Interface( fn=identify, inputs=[ gr.Audio(), ], outputs=gr.Label(num_top_classes=10), examples=LID_EXAMPLES, title="Language Identification", description=("Identity the language of input audio."), allow_flagging="never", ) tabbed_interface = gr.TabbedInterface( [mms_transcribe, mms_synthesize, mms_identify], ["Speech-to-text", "Text-to-speech", "Language Identification"], ) with gr.Blocks() as demo: gr.Markdown( "

MMS

" ) gr.HTML( """
Text-to-Speech, Speech-to-Text, and Language Recognition for 1,100+ languages.
""" ) tabbed_interface.render() if __name__ == "__main__": demo.queue() demo.launch()