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import gradio as gr
import torchaudio
import torch
import torch.nn.functional as F
from speechbrain.inference.speaker import EncoderClassifier
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
import noisereduce as nr
import librosa
# Load the classifier model
classifier = EncoderClassifier.from_hparams(source="speechbrain/spkrec-xvect-voxceleb", savedir="pretrained_models/spkrec-xvect-voxceleb")
def f2embed(wav_file, classifier, size_embed):
signal, fs = stereo_to_mono(wav_file)
if signal is None:
return None
# print(fs, "FS")
if fs != 16000:
signal, fs = resample_to_16000(signal, fs)
if signal is None:
return None
assert fs == 16000, fs
with torch.no_grad():
embeddings = classifier.encode_batch(signal)
embeddings = F.normalize(embeddings, dim=2)
embeddings = embeddings.squeeze().cpu().numpy()
assert embeddings.shape[0] == size_embed, embeddings.shape[0]
return embeddings
def stereo_to_mono(wav_file):
try:
signal, fs = torchaudio.load(wav_file)
signal_np = signal.numpy()
if signal_np.shape[0] == 2: # If stereo
signal_mono = librosa.to_mono(signal_np)
signal_mono = torch.from_numpy(signal_mono).unsqueeze(0)
else:
signal_mono = signal # Already mono
print(f"Converted to mono: {signal_mono.shape}")
return signal_mono, fs
except Exception as e:
print(f"Error in stereo_to_mono: {e}")
return None, None
def resample_to_16000(signal, original_sr):
try:
signal_np = signal.numpy().flatten()
signal_resampled = librosa.resample(signal_np, orig_sr=original_sr, target_sr=16000)
signal_resampled = torch.from_numpy(signal_resampled).unsqueeze(0)
print(f"Resampled to 16000 Hz: {signal_resampled.shape}")
return signal_resampled, 16000
except Exception as e:
print(f"Error in resample_to_16000: {e}")
return None, None
def reduce_noise(speech, noise_reduction_amount=0.5):
try:
denoised_speech = nr.reduce_noise(y=speech, sr=16000)
return denoised_speech
except Exception as e:
print(f"Error in reduce_noise: {e}")
return speech
def process_audio(wav_file, text):
try:
# Extract speaker embeddings
speaker_embeddings = f2embed(wav_file, classifier, 512)
if speaker_embeddings is None:
return None, "Error in speaker embedding extraction"
embeddings = torch.tensor(speaker_embeddings).unsqueeze(0)
# Load and process the speech file
signal, fs = torchaudio.load(wav_file)
signal_np = signal.numpy().flatten()
print(f"Loaded signal: {signal_np.shape}, Sample rate: {fs}")
# Convert text to speech using the speaker embeddings
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
inputs = processor(text=text, return_tensors="pt")
inputs.update({"speaker_embeddings": embeddings})
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
speech = model.generate_speech(inputs["input_ids"], speaker_embeddings=inputs["speaker_embeddings"],vocoder=vocoder)
print(f"Generated speech, shape: {speech.shape}")
# Reduce noise
speech_denoised = reduce_noise(speech)
print(f"Reduced noise, signal shape: {speech_denoised.shape}")
return speech_denoised, 16000
except Exception as e:
print(f"Error in process_audio: {e}")
return None, "Error in audio processing"
# Gradio interface
def gradio_interface(wav_file, text):
try:
processed_audio, rate = process_audio(wav_file, text)
if processed_audio is None:
return "Error occurred during processing"
return (rate, processed_audio)
except Exception as e:
print(f"Error in gradio_interface: {e}")
return "Error occurred during processing"
# Create Gradio interface
gr_interface = gr.Interface(
fn=gradio_interface,
inputs=[gr.Audio(type="filepath"), gr.Textbox(lines=2, placeholder="Enter text here...")],
outputs=gr.Audio(type="numpy"),
title="Text-to-Speech with Speaker Embeddings",
description="Upload a speaker audio file and enter text to convert the text to speech using the speaker's voice.",
)
gr_interface.launch()
# process_audio("/content/Network Chunck.mp3","Hello this network chunk") |