Update app.py

app.py

@@ -1,103 +1,103 @@
-import spaces
-import os
-from huggingface_hub import login
-import gradio as gr
-from cached_path import cached_path
-import tempfile
-from vinorm import TTSnorm
-
-from f5_tts.model import DiT
-from f5_tts.infer.utils_infer import (
-    preprocess_ref_audio_text,
-    load_vocoder,
-    load_model,
-    infer_process,
-    save_spectrogram,
-)
-
-# Retrieve token from secrets
-hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
-
-
-# Log in to Hugging Face
-if hf_token:
-    login(token=hf_token)
-
-def post_process(text):
-    text = " " + text + " "
-    text = text.replace(" . . ", " . ")
-    text = " " + text + " "
-    text = text.replace(" .. ", " . ")
-    text = " " + text + " "
-    text = text.replace(" , , ", " , ")
-    text = " " + text + " "
-    text = text.replace(" ,, ", " , ")
-    text = " " + text + " "
-    text = text.replace('"', "")
-    return " ".join(text.split())
-
-# Load models
-vocoder = load_vocoder()
-model = load_model(
-    DiT,
-    dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4),
-    ckpt_path=str(cached_path("hf://hynt/F5-TTS-Vietnamese-ViVoice/model_last.pt")),
-    vocab_file=str(cached_path("hf://hynt/F5-TTS-Vietnamese-ViVoice/config.json")),
-)
-
-@spaces.GPU
-def infer_tts(ref_audio_orig: str, gen_text: str, speed: float = 1.0, request: gr.Request = None):
-
-    if not ref_audio_orig:
-        raise gr.Error("Please upload a sample audio file.")
-    if not gen_text.strip():
-        raise gr.Error("Please enter the text content to generate voice.")
-    if len(gen_text.split()) > 1000:
-        raise gr.Error("Please enter text content with less than 1000 words.")
-    
-    try:
-        ref_audio, ref_text = preprocess_ref_audio_text(ref_audio_orig, "")
-        final_wave, final_sample_rate, spectrogram = infer_process(
-            ref_audio, ref_text.lower(), post_process(TTSnorm(gen_text)).lower(), model, vocoder, speed=speed
-        )
-        with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_spectrogram:
-            spectrogram_path = tmp_spectrogram.name
-            save_spectrogram(spectrogram, spectrogram_path)
-
-        return (final_sample_rate, final_wave), spectrogram_path
-    except Exception as e:
-        raise gr.Error(f"Error generating voice: {e}")
-
-# Gradio UI
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("""
-    # 🎤 F5-TTS: Vietnamese Text-to-Speech Synthesis.
-    # The model was trained with approximately 1000 hours of data on a RTX 3090 GPU. 
-    Enter text and upload a sample voice to generate natural speech.
-    """)
-    
-    with gr.Row():
-        ref_audio = gr.Audio(label="🔊 Sample Voice", type="filepath")
-        gen_text = gr.Textbox(label="📝 Text", placeholder="Enter the text to generate voice...", lines=3)
-    
-    speed = gr.Slider(0.3, 2.0, value=1.0, step=0.1, label="⚡ Speed")
-    btn_synthesize = gr.Button("🔥 Generate Voice")
-    
-    with gr.Row():
-        output_audio = gr.Audio(label="🎧 Generated Audio", type="numpy")
-        output_spectrogram = gr.Image(label="📊 Spectrogram")
-    
-    model_limitations = gr.Textbox(
-        value="""1. This model may not perform well with numerical characters, dates, special characters, etc. => A text normalization module is needed.
-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.
-3. Default, reference audio text uses the pho-whisper-medium model, which may not always accurately recognize Vietnamese, resulting in poor voice synthesis quality.
-4. Inference with overly long paragraphs may produce poor results.""", 
-        label="❗ Model Limitations",
-        lines=4,
-        interactive=False
-    )
-
-    btn_synthesize.click(infer_tts, inputs=[ref_audio, gen_text, speed], outputs=[output_audio, output_spectrogram])
-
-# Run Gradio with share=True to get a gradio.live link
-demo.queue().launch()
+import spaces
+import os
+from huggingface_hub import login
+import gradio as gr
+from cached_path import cached_path
+import tempfile
+from vinorm import TTSnorm
+
+from f5_tts.model import DiT
+from f5_tts.infer.utils_infer import (
+    preprocess_ref_audio_text,
+    load_vocoder,
+    load_model,
+    infer_process,
+    save_spectrogram,
+)
+
+# Retrieve token from secrets
+hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+
+# Log in to Hugging Face
+if hf_token:
+    login(token=hf_token)
+
+def post_process(text):
+    text = " " + text + " "
+    text = text.replace(" . . ", " . ")
+    text = " " + text + " "
+    text = text.replace(" .. ", " . ")
+    text = " " + text + " "
+    text = text.replace(" , , ", " , ")
+    text = " " + text + " "
+    text = text.replace(" ,, ", " , ")
+    text = " " + text + " "
+    text = text.replace('"', "")
+    return " ".join(text.split())
+
+# Load models
+vocoder = load_vocoder()
+model = load_model(
+    DiT,
+    dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4),
+    ckpt_path=str(cached_path("hf://hynt/F5-TTS-Vietnamese-ViVoice/model_last.pt")),
+    vocab_file=str(cached_path("hf://hynt/F5-TTS-Vietnamese-ViVoice/config.json")),
+)
+
+@spaces.GPU
+def infer_tts(ref_audio_orig: str, gen_text: str, speed: float = 1.0, request: gr.Request = None):
+
+    if not ref_audio_orig:
+        raise gr.Error("Please upload a sample audio file.")
+    if not gen_text.strip():
+        raise gr.Error("Please enter the text content to generate voice.")
+    if len(gen_text.split()) > 1000:
+        raise gr.Error("Please enter text content with less than 1000 words.")
+    
+    try:
+        ref_audio, ref_text = preprocess_ref_audio_text(ref_audio_orig, "")
+        final_wave, final_sample_rate, spectrogram = infer_process(
+            ref_audio, ref_text.lower(), post_process(TTSnorm(gen_text)).lower(), model, vocoder, speed=speed
+        )
+        with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_spectrogram:
+            spectrogram_path = tmp_spectrogram.name
+            save_spectrogram(spectrogram, spectrogram_path)
+
+        return (final_sample_rate, final_wave), spectrogram_path
+    except Exception as e:
+        raise gr.Error(f"Error generating voice: {e}")
+
+# Gradio UI
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🎤 F5-TTS: Vietnamese Text-to-Speech Synthesis.
+    # The model was trained with approximately 1000 hours of data on a RTX 3090 GPU. 
+    Enter text and upload a sample voice to generate natural speech.
+    """)
+    
+    with gr.Row():
+        ref_audio = gr.Audio(label="🔊 Sample Voice", type="filepath")
+        gen_text = gr.Textbox(label="📝 Text", placeholder="Enter the text to generate voice...", lines=3)
+    
+    speed = gr.Slider(0.3, 2.0, value=1.0, step=0.1, label="⚡ Speed")
+    btn_synthesize = gr.Button("🔥 Generate Voice")
+    
+    with gr.Row():
+        output_audio = gr.Audio(label="🎧 Generated Audio", type="numpy")
+        output_spectrogram = gr.Image(label="📊 Spectrogram")
+    
+    model_limitations = gr.Textbox(
+        value="""1. This model may not perform well with numerical characters, dates, special characters, etc. => A text normalization module is needed.
+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.
+3. Default, reference audio text uses the pho-whisper-medium model, which may not always accurately recognize Vietnamese, resulting in poor voice synthesis quality.
+4. Inference with overly long paragraphs may produce poor results.""", 
+        label="❗ Model Limitations",
+        lines=4,
+        interactive=False
+    )
+
+    btn_synthesize.click(infer_tts, inputs=[ref_audio, gen_text, speed], outputs=[output_audio, output_spectrogram])
+
+# Run Gradio with share=True to get a gradio.live link
+demo.queue().launch(share=True)