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Delete app.py

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  1. app.py +0 -56
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- import gradio as gr
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- import torch
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- import tempfile
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- import soundfile as sf
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- from tortoise.api import TextToSpeech
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- from tortoise.utils.audio import load_audio
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-
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- # 1) Initialize the Tortoise TTS engine at startup
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- tts = TextToSpeech() # Downloads and caches models automatically
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-
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- # 2) Define a helper to generate speech from a reference clip + text
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- def generate_speech(reference_audio_path, text):
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- """
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- reference_audio_path: filepath to a WAV sampled at 22 050 Hz
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- text: the string to synthesize
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- returns: path to a 24 kHz WAV file with your cloned voice
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- """
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- # βœ… FIXED: Provide sampling_rate as a required positional argument
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- ref_waveform = load_audio(reference_audio_path, 22050)
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-
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- # Generate speech using 'fast' preset (alternatives: ultra_fast, standard, high_quality)
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- output_tensor = tts.tts_with_preset(
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- text,
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- voice_samples=[ref_waveform],
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- preset="fast"
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- )
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-
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- # Save to temp WAV (float32, 24 kHz)
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- wav_np = output_tensor.squeeze().cpu().numpy()
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- tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
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- sf.write(tmp.name, wav_np, samplerate=24000)
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- return tmp.name
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-
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- # 3) Build the Gradio interface
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- with gr.Blocks(title="Tortoise Voice Cloning TTS") as app:
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- gr.Markdown("## πŸ—£οΈ Voice Cloning with Tortoise TTS")
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- gr.Markdown(
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- "Upload a ~10 sec WAV clip (22 050 Hz), enter English text, "
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- "and hear it spoken back in **your** voice!"
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- )
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-
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- with gr.Row():
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- voice_sample = gr.Audio(type="filepath", label="πŸŽ™οΈ Upload Reference Voice (22 050 Hz WAV)")
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- text_input = gr.Textbox(label="πŸ’¬ Text to Synthesize", placeholder="e.g., Hello, world!")
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-
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- generate_btn = gr.Button("πŸ”Š Generate Speech")
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- output_audio = gr.Audio(label="πŸ“’ Cloned Speech Output (24 kHz)", interactive=False)
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-
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- generate_btn.click(
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- fn=generate_speech,
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- inputs=[voice_sample, text_input],
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- outputs=output_audio
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- )
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-
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- if __name__ == "__main__":
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- app.launch()