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import gradio as gr
import torchaudio
import torch
# Define the list of target languages
languages = {
"English": "eng",
"Hindi": "hin",
"Portuguese": "por",
"Russian": "rus",
"Spanish": "spa"
}
def speech_to_text(audio_data, tgt_lang):
audio_input, _ = torchaudio.load(audio_data)
s2t_model = torch.jit.load("unity_on_device_s2t.ptl")
with torch.no_grad():
text = s2t_model(audio_input, tgt_lang=languages[tgt_lang])
return text
def speech_to_speech_translation(audio_data, tgt_lang):
audio_input, _ = torchaudio.load(audio_data)
s2st_model = torch.jit.load("unity_on_device.ptl")
with torch.no_grad():
text, units, waveform = s2st_model(audio_input, tgt_lang=languages[tgt_lang])
output_file = "/tmp/result.wav"
torchaudio.save(output_file, waveform.unsqueeze(0), sample_rate=16000)
return text, output_file
# Gradio interfaces
iface_s2t = gr.Interface(
fn=speech_to_text,
inputs=[
gr.inputs.Audio(label="Upload or Record Audio for Speech to Text"),
gr.inputs.Dropdown(list(languages.keys()), label="Select Target Language")
],
outputs="text",
title="Speech to Text"
)
iface_s2st = gr.Interface(
fn=speech_to_speech_translation,
inputs=[
gr.inputs.Audio(label="Upload or Record Audio for Speech to Speech Translation"),
gr.inputs.Dropdown(list(languages.keys()), label="Select Target Language")
],
outputs=["text", "audio"],
title="Speech to Speech Translation"
)
# Combine into an accordion interface
accordion = gr.Accordion(
iface_s2t,
iface_s2st,
labels=["Speech to Text", "Speech to Speech Translation"]
)
# Launch the application
accordion.launch()
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