File size: 4,635 Bytes
7bcf8d7 bec2b9c 7bcf8d7 5cc287f 7bcf8d7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 |
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
demo = gr.Blocks()
mms_select_source_trans = gr.Radio(
["Record from Mic", "Upload audio"],
label="Audio input",
value="Record from Mic",
)
mms_mic_source_trans = gr.Audio(source="microphone", type="filepath", label="Use mic")
mms_upload_source_trans = gr.Audio(
source="upload", type="filepath", label="Upload file", visible=False
)
mms_transcribe = gr.Interface(
fn=transcribe,
inputs=[
mms_select_source_trans,
mms_mic_source_trans,
mms_upload_source_trans,
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_select_source_iden = gr.Radio(
["Record from Mic", "Upload audio"],
label="Audio input",
value="Record from Mic",
)
mms_mic_source_iden = gr.Audio(source="microphone", type="filepath", label="Use mic")
mms_upload_source_iden = gr.Audio(
source="upload", type="filepath", label="Upload file", visible=False
)
mms_identify = gr.Interface(
fn=identify,
inputs=[
mms_select_source_iden,
mms_mic_source_iden,
mms_upload_source_iden,
],
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><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()
mms_select_source_trans.change(
lambda x: [
gr.update(visible=True if x == "Record from Mic" else False),
gr.update(visible=True if x == "Upload audio" else False),
],
inputs=[mms_select_source_trans],
outputs=[mms_mic_source_trans, mms_upload_source_trans],
queue=False,
)
mms_select_source_iden.change(
lambda x: [
gr.update(visible=True if x == "Record from Mic" else False),
gr.update(visible=True if x == "Upload audio" else False),
],
inputs=[mms_select_source_iden],
outputs=[mms_mic_source_iden, mms_upload_source_iden],
queue=False,
)
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>
"""
)
demo.queue(concurrency_count=3)
demo.launch()
|