Update app.py
Browse files
app.py
CHANGED
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import streamlit as st
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import torch
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import torchaudio
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import numpy as np
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import librosa
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import soundfile as sf
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import
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import os
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from io import BytesIO
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import tempfile
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import huggingface_hub
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class VoiceConverter:
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def __init__(self):
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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self.load_models()
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def load_models(self):
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# Download pre-trained models if not exists
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models_dir = "pretrained_models"
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os.makedirs(models_dir, exist_ok=True)
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# Load Coqui TTS model
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self.tts = TTS("tts_models/multilingual/multi-dataset/your_tts", progress_bar=False)
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# Load VITS model
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vits_path = os.path.join(models_dir, "vits_female.pth")
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if not os.path.exists(vits_path):
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# Download VITS pre-trained model
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wget.download(
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"https://huggingface.co/spaces/sayashi/vits-uma-genshin-honkai/resolve/main/G_953000.pth",
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vits_path
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)
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self.vits_model = torch.load(vits_path, map_location=self.device)
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self.vits_model.eval()
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#
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# Convert to tensor
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wav_tensor = torch.FloatTensor(wav).unsqueeze(0).to(self.device)
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# Process with VITS
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with torch.no_grad():
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converted = self.vits_model.voice_conversion(
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wav_tensor,
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speaker_id=speaker_id
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)
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# Process with Coqui TTS for emotion
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wav_path = "temp.wav"
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sf.write(wav_path, converted.cpu().numpy(), sr)
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)
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#
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uploaded_file = st.file_uploader("Upload an audio file", type=['wav', 'mp3'])
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if uploaded_file is not None:
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# Initialize converter
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converter = VoiceConverter()
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# Save uploaded file temporarily
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with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp_file:
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tmp_file.write(uploaded_file.getvalue())
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tmp_path = tmp_file.name
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)
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#
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# Display audio player
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st.audio(
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# Download button
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st.download_button(
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label="Download Converted Audio",
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data=
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file_name="
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mime="audio/wav"
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)
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except Exception as e:
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st.error(f"Error during conversion: {str(e)}")
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# Add information about the models
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st.markdown("""
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###
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- **Natural Female**: Realistic female voice
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- **Young Female**: Young adult female voice
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- **Mature Female**: Mature female voice
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### Tips for Best Results:
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"""
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pip install requirements:
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TTS
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fairseq
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torch
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torchaudio
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streamlit
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librosa
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soundfile
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numpy
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wget
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huggingface_hub
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"""
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import streamlit as st
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import librosa
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import soundfile as sf
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import numpy as np
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import scipy.signal as signal
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from scipy.io import wavfile
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from io import BytesIO
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import tempfile
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def modify_formants(y, sr, formant_shift_factor=1.2):
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# Get the power spectrum
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D = librosa.stft(y)
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S = np.abs(D)
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# Use frame-based processing for LPC
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frame_length = 2048
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hop_length = 512
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frames = librosa.util.frame(y, frame_length=frame_length, hop_length=hop_length)
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# Process each frame
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modified_frames = []
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for frame in frames.T:
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# Calculate LPC coefficients
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a = librosa.lpc(frame, order=12)
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# Shift formants
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new_a = np.zeros_like(a)
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new_a[0] = a[0]
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for i in range(1, len(a)):
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new_a[i] = a[i] * (formant_shift_factor ** i)
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# Apply modified LPC filter
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modified_frame = signal.lfilter([1], new_a, frame)
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modified_frames.append(modified_frame)
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# Reconstruct the signal
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y_formant = np.concatenate([frame[:hop_length] for frame in modified_frames[:-1]] +
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[modified_frames[-1]])
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return librosa.util.normalize(y_formant)
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def enhance_harmonics(y, sr):
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# Extract harmonics using harmonic-percussive source separation
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y_harmonic = librosa.effects.hpss(y)[0]
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# Enhance the harmonics
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y_enhanced = y_harmonic * 1.2 + y * 0.3
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return librosa.util.normalize(y_enhanced)
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def process_audio_advanced(audio_file, settings):
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# Load audio
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y, sr = librosa.load(audio_file)
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# Pitch shifting with formant preservation
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y_shifted = librosa.effects.pitch_shift(
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y,
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sr=sr,
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n_steps=settings['pitch_shift']
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)
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# Modify formants
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y_formant = modify_formants(
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y_shifted,
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sr,
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settings['formant_shift']
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)
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# Enhance harmonics
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y_harmonic = enhance_harmonics(y_formant, sr)
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# Apply vocal tract length modification through resampling
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y_vtln = librosa.effects.time_stretch(
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y_harmonic,
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rate=settings['vtln_factor']
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)
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# Smooth the output
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y_smooth = signal.savgol_filter(y_vtln, 1001, 2)
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# Final normalization
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y_final = librosa.util.normalize(y_smooth)
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return y_final, sr
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def create_voice_preset(preset_name):
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presets = {
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'Young Female': {
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'pitch_shift': 8.0,
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'formant_shift': 1.3,
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'vtln_factor': 1.1,
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'breathiness': 0.3
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},
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'Mature Female': {
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'pitch_shift': 6.0,
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'formant_shift': 1.2,
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'vtln_factor': 1.05,
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'breathiness': 0.2
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},
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'Soft Female': {
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'pitch_shift': 7.0,
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'formant_shift': 1.25,
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'vtln_factor': 1.15,
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'breathiness': 0.4
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}
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}
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return presets.get(preset_name)
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def add_breathiness(y, sr, amount=0.3):
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# Generate breath noise
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noise = np.random.normal(0, 0.01, len(y))
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noise_filtered = signal.lfilter([1], [1, -0.98], noise)
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# Mix with original signal
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y_breathy = y * (1 - amount) + noise_filtered * amount
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return librosa.util.normalize(y_breathy)
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st.title("Advanced Female Voice Converter")
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# File uploader
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uploaded_file = st.file_uploader("Upload an audio file", type=['wav', 'mp3'])
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if uploaded_file is not None:
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# Save uploaded file temporarily
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with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp_file:
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tmp_file.write(uploaded_file.getvalue())
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tmp_path = tmp_file.name
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# Voice preset selector
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preset_name = st.selectbox(
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"Select Voice Preset",
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['Young Female', 'Mature Female', 'Soft Female', 'Custom']
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)
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if preset_name == 'Custom':
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settings = {
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'pitch_shift': st.slider("Pitch Shift", 0.0, 12.0, 8.0, 0.5),
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'formant_shift': st.slider("Formant Shift", 1.0, 1.5, 1.2, 0.05),
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'vtln_factor': st.slider("Vocal Tract Length", 0.9, 1.2, 1.1, 0.05),
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'breathiness': st.slider("Breathiness", 0.0, 1.0, 0.3, 0.1)
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}
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else:
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settings = create_voice_preset(preset_name)
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if st.button("Convert Voice"):
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with st.spinner("Processing audio..."):
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try:
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# Process audio
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processed_audio, sr = process_audio_advanced(tmp_path, settings)
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# Add breathiness
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processed_audio = add_breathiness(
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processed_audio,
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sr,
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settings['breathiness']
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# Save to buffer
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buffer = BytesIO()
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sf.write(buffer, processed_audio, sr, format='WAV')
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# Display audio player
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st.audio(buffer, format='audio/wav')
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# Download button
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st.download_button(
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label="Download Converted Audio",
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data=buffer,
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file_name="female_voice_converted.wav",
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mime="audio/wav"
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)
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except Exception as e:
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st.error(f"Error processing audio: {str(e)}")
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st.markdown("""
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### Voice Conversion Features:
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- Pitch shifting with formant preservation
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- Harmonic enhancement
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- Vocal tract length modification
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- Natural breathiness addition
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- Multiple voice presets
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- Custom parameter controls
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### Tips for Best Results:
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1. Start with a clear audio recording
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2. Try different presets to find the best match
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3. For custom settings:
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- Pitch shift: 6-8 for natural female voice
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- Formant shift: 1.1-1.3 for feminine resonance
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- Vocal tract length: 1.05-1.15 for realistic results
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- Breathiness: 0.2-0.4 for natural sound
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""")
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