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( "<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>" ) gr.HTML( """<center>Click on the appropriate tab to explore Speech-to-text (ASR), Text-to-speech (TTS) and Language identification (LID) demos. </center>""" ) gr.HTML( """<center>You can also finetune MMS models on your data using the recipes provides here - <a href='https://huggingface.co/blog/mms_adapters'>ASR</a> <a href='https://github.com/ylacombe/finetune-hf-vits'>TTS</a> </center>""" ) gr.HTML( """<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>""" ) tabbed_interface.render() gr.HTML( """ <div class="footer" style="text-align:center"> <p> Model by <a href="https://ai.facebook.com" style="text-decoration: underline;" target="_blank">Meta AI</a> - Gradio Demo by 🤗 Hugging Face </p> </div> """ ) if __name__ == "__main__": demo.queue() demo.launch()