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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() |