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Update app.py
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app.py
CHANGED
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@@ -71,7 +71,7 @@ def infer_tts(ref_audio_orig: str, gen_text: str, speed: float = 1.0, request: g
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with gr.Blocks(theme=gr.themes.Soft()) as demo:
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gr.Markdown("""
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# 🎤 F5-TTS: Vietnamese Text-to-Speech Synthesis.
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# The model was trained
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Enter text and upload a sample voice to generate natural speech.
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""")
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@@ -90,10 +90,9 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
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value="""1. This model may not perform well with numerical characters, dates, special characters, etc. => A text normalization module is needed.
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2. The rhythm of some generated audios may be inconsistent or choppy => It is recommended to select clearly pronounced sample audios with minimal pauses for better synthesis quality.
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3. Default, reference audio text uses the whisper-large-v3-turbo model, which may not always accurately recognize Vietnamese, resulting in poor voice synthesis quality.
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4.
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5. Inference with overly long paragraphs may produce poor results.""",
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label="❗ Model Limitations",
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lines=
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interactive=False
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)
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with gr.Blocks(theme=gr.themes.Soft()) as demo:
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gr.Markdown("""
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# 🎤 F5-TTS: Vietnamese Text-to-Speech Synthesis.
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# The model was trained with approximately 1000 hours of data on an RTX 3090 GPU.
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Enter text and upload a sample voice to generate natural speech.
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""")
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value="""1. This model may not perform well with numerical characters, dates, special characters, etc. => A text normalization module is needed.
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2. The rhythm of some generated audios may be inconsistent or choppy => It is recommended to select clearly pronounced sample audios with minimal pauses for better synthesis quality.
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3. Default, reference audio text uses the whisper-large-v3-turbo model, which may not always accurately recognize Vietnamese, resulting in poor voice synthesis quality.
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4. Inference with overly long paragraphs may produce poor results.""",
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label="❗ Model Limitations",
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lines=4,
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interactive=False
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)
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