Update app.py
Browse files
app.py
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@@ -2,69 +2,27 @@ import gradio as gr
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from TTS.api import TTS
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import time
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#
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"FastPitch (Female - LJSpeech)": "tts_models/en/ljspeech/fast_pitch",
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"Glow-TTS (Female - LJSpeech)": "tts_models/en/ljspeech/glow-tts",
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"Tactron2 (Female - LJSpeaker)": "tts_models/en/ljspeech/tacotron2-DDC",
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"VCTK (Multi-speaker)": "tts_models/en/vctk/vits",
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"YourTTS (Cloning + Multi-speaker)": "tts_models/multilingual/multi-dataset/your_tts",
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}
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#
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# Language display name -> model language code
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language_map = {
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"English": "en",
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"French": "fr-fr",
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"Portuguese": "pt-br",
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"Hindi": "hi", # Not supported in YourTTS
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"Japanese": "ja" # Not supported in YourTTS
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}
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# Supported languages for YourTTS
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yourtts_supported_languages = ["en", "fr-fr", "pt-br"]
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# Initial model setup
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current_model_key = list(default_models.values())[0]
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tts = TTS(current_model_key, gpu=False)
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def synthesize(text, selected_model, speaker_id, custom_model_url, speaker_wav_path, selected_language):
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global tts, current_model_key
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model_path = custom_model_url if custom_model_url else default_models[selected_model]
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# Load the model only if different from current
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if model_path != current_model_key:
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tts = TTS(model_path, gpu=False)
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current_model_key = model_path
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output_path = "output.wav"
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start_time = time.time()
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try:
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tts.tts_to_file(text=text, speaker_wav=speaker_wav_path, file_path=output_path, language=lang_code)
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speaker_info = f"WAV Upload: {speaker_wav_path.split('/')[-1]}"
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elif "vctk" in model_path.lower() and speaker_id and speaker_id != "None":
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tts.tts_to_file(text=text, speaker=speaker_id, file_path=output_path)
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speaker_info = speaker_id
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else:
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tts.tts_to_file(text=text, file_path=output_path)
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except ValueError as e:
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return None, {"error": str(e)}
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total_time = time.time() - start_time
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@@ -72,63 +30,50 @@ def synthesize(text, selected_model, speaker_id, custom_model_url, speaker_wav_p
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rtf = round(total_time / est_duration, 3)
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return output_path, {
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"
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"processing_time_sec": round(total_time, 3),
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"real_time_factor": rtf,
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"model_used":
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"speaker_used":
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}
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# Gradio UI
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with gr.Blocks() as demo:
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gr.Markdown("## π£οΈ
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choices=list(language_map.keys()),
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value="English",
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label="Select Language"
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)
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with gr.Row():
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model_dropdown = gr.Dropdown(choices=list(default_models.keys()), label="Select TTS Model")
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speaker_dropdown = gr.Dropdown(choices=["None"] + vctk_speakers, label="Speaker ID (for VCTK)")
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generate_btn = gr.Button("π Generate Speech")
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output_audio = gr.Audio(label="Output Audio", type="filepath")
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metadata_json = gr.JSON(label="Meta Info (Time, Model, RTF,
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generate_btn.click(
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fn=synthesize,
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inputs=[input_text,
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outputs=[output_audio, metadata_json]
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)
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# API Interface
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api = gr.Interface(
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fn=synthesize,
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inputs=[
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gr.Text(),
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gr.
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gr.Text(), # speaker id
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gr.Text(), # custom model url
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gr.Audio(type="filepath"), # speaker wav
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gr.Text() # language
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],
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outputs=[gr.Audio(type="filepath"), gr.JSON()],
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)
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# Launch
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demo.queue()
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api.queue()
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demo.launch()
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from TTS.api import TTS
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import time
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# Fixed model (YourTTS in English)
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YOURTTS_MODEL = "tts_models/multilingual/multi-dataset/your_tts"
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# Initialize model once
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tts = TTS(YOURTTS_MODEL, gpu=False)
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def synthesize(text, speaker_wav_path):
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output_path = "output.wav"
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start_time = time.time()
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if not speaker_wav_path:
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return None, {"error": "β Please upload a speaker WAV file for cloning."}
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try:
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tts.tts_to_file(
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text=text,
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speaker_wav=speaker_wav_path,
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file_path=output_path,
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language="en"
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)
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except Exception as e:
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return None, {"error": str(e)}
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total_time = time.time() - start_time
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rtf = round(total_time / est_duration, 3)
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return output_path, {
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"language": "English",
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"processing_time_sec": round(total_time, 3),
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"real_time_factor": rtf,
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"model_used": YOURTTS_MODEL,
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"speaker_used": speaker_wav_path.split("/")[-1]
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}
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# Gradio UI
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with gr.Blocks() as demo:
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gr.Markdown("## π£οΈ YourTTS Voice Cloning (English Only)")
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input_text = gr.Textbox(
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label="Text",
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placeholder="Type something to synthesize...",
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lines=3
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)
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speaker_wav = gr.Audio(
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label="Upload Speaker Voice (WAV, 5β10s)",
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type="filepath"
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)
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generate_btn = gr.Button("π Generate Speech")
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output_audio = gr.Audio(label="Output Audio", type="filepath")
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metadata_json = gr.JSON(label="Meta Info (Time, Model, RTF, etc.)")
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generate_btn.click(
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fn=synthesize,
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inputs=[input_text, speaker_wav],
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outputs=[output_audio, metadata_json]
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)
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# API interface (English only)
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api = gr.Interface(
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fn=synthesize,
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inputs=[
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gr.Text(), # text
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gr.Audio(type="filepath") # speaker wav
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],
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outputs=[gr.Audio(type="filepath"), gr.JSON()],
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)
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# Launch app
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demo.queue()
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api.queue()
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demo.launch()
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