Update app.py

app.py

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+# rvc_advanced.py
+import torch
+import torchaudio
+import librosa
+import sounddevice as sd
+import streamlit as st
+from transformers import HubertModel, HubertProcessor
+from speechbrain.inference import HIFIGAN
+
+# ----------------------------
+# Load Pretrained Models
+# ----------------------------
+st.write("Loading models, please wait...")
+processor = HubertProcessor.from_pretrained("facebook/hubert-large-ls960-ft")
+hubert_model = HubertModel.from_pretrained("facebook/hubert-large-ls960-ft")
+vocoder = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="tmp_hifigan")
+
+# ----------------------------
+# Audio Processing Functions
+# ----------------------------
+def extract_speaker_embedding(audio_path):
+    y, sr = librosa.load(audio_path, sr=16000)
+    inputs = processor(y, sampling_rate=sr, return_tensors="pt", padding=True)
+    with torch.no_grad():
+        embeddings = hubert_model(**inputs).last_hidden_state.mean(dim=1)
+    return embeddings
+
+def audio_to_mel(audio_path):
+    waveform, sample_rate = torchaudio.load(audio_path)
+    mel_spec = torchaudio.transforms.MelSpectrogram(sample_rate)(waveform)
+    return mel_spec
+
+def mel_to_audio(mel_spec):
+    # Using vocoder to convert mel spectrogram to waveform
+    waveform = vocoder.decode_batch(mel_spec)
+    return waveform
+
+def convert_voice(input_path, reference_path):
+    ref_emb = extract_speaker_embedding(reference_path)
+    mel_spec = audio_to_mel(input_path)
+    # Apply speaker embedding to mel spectrogram
+    converted_mel = mel_spec + ref_emb.unsqueeze(-1)
+    waveform = mel_to_audio(converted_mel)
+    return waveform
+
+def play_audio(waveform, sample_rate=16000):
+    sd.play(waveform.squeeze().cpu().numpy(), sample_rate)
+    sd.wait()
+
+# ----------------------------
+# Streamlit GUI
+# ----------------------------
+st.title("Advanced RVC Voice Converter")
+
+input_audio = st.file_uploader("Upload Input Audio", type=["wav", "mp3"])
+reference_audio = st.file_uploader("Upload Reference Audio", type=["wav", "mp3"])
+
+if input_audio and reference_audio:
+    if st.button("Convert Voice"):
+        # Save temp files
+        input_path = "temp_input.wav"
+        reference_path = "temp_reference.wav"
+        with open(input_path, "wb") as f:
+            f.write(input_audio.read())
+        with open(reference_path, "wb") as f:
+            f.write(reference_audio.read())
+        
+        st.write("Converting voice...")
+        waveform = convert_voice(input_path, reference_path)
+        st.write("Playing converted audio...")
+        play_audio(waveform)
+        st.audio(waveform.squeeze().cpu().numpy(), format="audio/wav")