Upload 2 files
Browse files- app.py +56 -0
- requirements.txt +11 -0
app.py
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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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# 1) Initialize the Tortoise TTS engine at startup
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tts = TextToSpeech() # Downloads and caches models automatically
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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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# β
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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# 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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# 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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# 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("## π£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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with gr.Row():
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voice_sample = gr.Audio(type="filepath", label="ποΈ Upload Reference Voice (22 050 Hz WAV)")
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text_input = gr.Textbox(label="π¬ Text to Synthesize", placeholder="e.g., Hello, world!")
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generate_btn = gr.Button("π Generate Speech")
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output_audio = gr.Audio(label="π’ Cloned Speech Output (24 kHz)", interactive=False)
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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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if __name__ == "__main__":
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app.launch()
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requirements.txt
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torch
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torchaudio
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transformers
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datasets
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librosa
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soundfile
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numpy
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sentencepiece
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TTS
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tortoise-tts
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pycryptodome
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