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import gradio as gr
import torchaudio
import speechbrain
from speechbrain.pretrained import EncoderClassifier, Tacotron2, HIFIGAN, ASR
import os
import soundfile as sf
# Ensure output directory exists
os.makedirs("output_audio", exist_ok=True)
# Load models
encoder = EncoderClassifier.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb", savedir="models/encoder")
tacotron2 = Tacotron2.from_hparams(source="speechbrain/tts-tacotron2-ljspeech", savedir="models/tacotron2")
hifigan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="models/hifigan")
asr = ASR.from_hparams(source="speechbrain/asr-transformer-librispeech", savedir="models/asr")
def speech_to_text(input_audio):
sig, sr = torchaudio.load(input_audio)
transcription = asr.transcribe_file(input_audio)
return transcription
def speech_to_speech(input_audio, target_text):
# Load and encode speaker from input audio
signal, fs = torchaudio.load(input_audio)
if fs != 16000:
signal = torchaudio.transforms.Resample(orig_freq=fs, new_freq=16000)(signal)
embedding = encoder.encode_batch(signal)
# Synthesize speech from text
mel_output, mel_length, alignment = tacotron2.encode_text(target_text, embedding)
waveform = hifigan.decode_batch(mel_output)
# Save output audio
output_path = "output_audio/synthesized_speech.wav"
sf.write(output_path, waveform.squeeze().cpu().numpy(), 22050)
return output_path
def text_to_speech(text):
mel_output, mel_length, alignment = tacotron2.encode_text(text)
waveform = hifigan.decode_batch(mel_output)
output_path = "output_audio/text_to_speech.wav"
sf.write(output_path, waveform.squeeze().cpu().numpy(), 22050)
return output_path
iface = gr.Interface(
fn={
"Speech to Text": speech_to_text,
"Text to Speech": text_to_speech,
"Speech to Speech": speech_to_speech
},
inputs={
"Speech to Text": gr.inputs.Audio(source="upload", type="file"),
"Text to Speech": gr.inputs.Textbox(label="Text"),
"Speech to Speech": [gr.inputs.Audio(source="upload", type="file"), gr.inputs.Textbox(label="Target Text")]
},
outputs={
"Speech to Text": gr.outputs.Textbox(label="Transcription"),
"Text to Speech": gr.outputs.Audio(type="file", label="Synthesized Speech"),
"Speech to Speech": gr.outputs.Audio(type="file", label="Synthesized Speech")
},
title="Speech Processing App",
description="Upload an audio file or enter text to perform various speech processing tasks.",
layout="vertical"
)
if __name__ == "__main__":
iface.launch()
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