Update app.py

app.py

@@ -2,214 +2,81 @@ import logging
 import os
 import time
 import uuid
-
 import gradio as gr
 import soundfile as sf
-
 from model import get_pretrained_model, language_to_models
 
 title = "# Next-gen Kaldi: Text-to-speech (TTS)"
 
 description = """
 This space shows how to convert text to speech with Next-gen Kaldi.
-
 It is running on CPU within a docker container provided by Hugging Face.
-
 See more information by visiting the following links:
-
 - <https://github.com/k2-fsa/sherpa-onnx>
-
-If you want to deploy it locally, please see
-<https://k2-fsa.github.io/sherpa/>
-
-If you want to use Android APKs, please see
-<https://k2-fsa.github.io/sherpa/onnx/tts/apk.html>
-
-If you want to use Android text-to-speech engine APKs, please see
-<https://k2-fsa.github.io/sherpa/onnx/tts/apk-engine.html>
-
-If you want to download an all-in-one exe for Windows, please see
-<https://github.com/k2-fsa/sherpa-onnx/releases/tag/tts-models>
+If you want to deploy it locally, please see <https://k2-fsa.github.io/sherpa/>
+If you want to use Android APKs, please see <https://k2-fsa.github.io/sherpa/onnx/tts/apk.html>
+If you want to use Android text-to-speech engine APKs, please see <https://k2-fsa.github.io/sherpa/onnx/tts/apk-engine.html>
+If you want to download an all-in-one exe for Windows, please see <https://github.com/k2-fsa/sherpa-onnx/releases/tag/tts-models>
 """
 
-css = """
-.result {display:flex;flex-direction:column}
-.result_item {padding:15px;margin-bottom:8px;border-radius:15px;width:100%}
-.result_item_success {background-color:mediumaquamarine;color:white;align-self:start}
-.result_item_error {background-color:#ff7070;color:white;align-self:start}
-"""
+css = """.result {display:flex;flex-direction:column}.result_item {padding:15px;margin-bottom:8px;border-radius:15px;width:100%}.result_item_success {background-color:mediumaquamarine;color:white;align-self:start}.result_item_error {background-color:#ff7070;color:white;align-self:start}"""
 
-# Simplified examples for Portuguese only
-examples = [
-    ["Portuguese", "csukuangfj/vits-mms-por", "Eu desejo uma versão simplificada para português.", 0, 1.0],
-]
+examples = [["Portuguese", "csukuangfj/vits-mms-por", "Eu desejo uma versão simplificada para português.", 0, 1.0]]
 
-# Use only Portuguese as a language choice
 language_choices = ["Portuguese"]
 
-def update_model_dropdown(language: str):
-    if language in language_to_models:
-        choices = language_to_models[language]
-        return gr.Dropdown(
-            choices=choices,
-            value=choices[0],
-            interactive=True,
-        )
-
-    raise ValueError(f"Unsupported language: {language}")
-
-
-def build_html_output(s: str, style: str = "result_item_success"):
-    return f"""
-    <div class='result'>
-        <div class='result_item {style}'>
-          {s}
-        </div>
-    </div>
-    """
+def update_model_dropdown(language):
+    return gr.Dropdown(choices=language_to_models.get(language, []), value=language_to_models.get(language, [""])[0], interactive=True)
 
+def build_html_output(s, style="result_item_success"):
+    return f"""<div class='result'><div class='result_item {style}'>{s}</div></div>"""
 
-def process(language: str, repo_id: str, text: str, sid: str, speed: float):
+def process(language, repo_id, text, sid, speed):
     logging.info(f"Input text: {text}. sid: {sid}, speed: {speed}")
     sid = int(sid)
     tts = get_pretrained_model(repo_id, speed)
-
     start = time.time()
     audio = tts.generate(text, sid=sid)
     end = time.time()
-
     if len(audio.samples) == 0:
-        raise ValueError(
-            "Error in generating audios. Please read previous error messages."
-        )
-
+        raise ValueError("Error in generating audios. Please read previous error messages.")
     duration = len(audio.samples) / audio.sample_rate
-
     elapsed_seconds = end - start
     rtf = elapsed_seconds / duration
-
-    info = f"""
-    Wave duration  : {duration:.3f} s <br/>
-    Processing time: {elapsed_seconds:.3f} s <br/>
-    RTF: {elapsed_seconds:.3f}/{duration:.3f} = {rtf:.3f} <br/>
-    """
-
+    info = f"""Wave duration  : {duration:.3f} s <br/>Processing time: {elapsed_seconds:.3f} s <br/>RTF: {elapsed_seconds:.3f}/{duration:.3f} = {rtf:.3f} <br/>"""
     logging.info(info)
     logging.info(f"\nrepo_id: {repo_id}\ntext: {text}\nsid: {sid}\nspeed: {speed}")
-
-    filename = str(uuid.uuid4())
-    filename = f"{filename}.wav"
-    sf.write(
-        filename,
-        audio.samples,
-        samplerate=audio.sample_rate,
-        subtype="PCM_16",
-    )
-
+    filename = str(uuid.uuid4()) + ".wav"
+    sf.write(filename, audio.samples, samplerate=audio.sample_rate, subtype="PCM_16")
     return filename, build_html_output(info)
 
-
 demo = gr.Blocks(css=css)
 
 with demo:
     gr.Markdown(title)
-    
-    # Use Radio instead of Dropdown for language choice
-    language_radio = gr.Radio(
-        label="Language",
-        choices=language_choices,
-        value=language_choices[0],
-    )
-
-    # Initialize model_dropdown with Portuguese models
-    model_dropdown = gr.Dropdown(
-        choices=language_to_models["Portuguese"],
-        label="Select a model",
-        value=language_to_models["Portuguese"][0],
-    )
-
-    # No need to update model_dropdown for a single language
+    language_radio = gr.Radio(label="Language", choices=language_choices, value=language_choices[0])
+    model_dropdown = gr.Dropdown(choices=language_to_models["Portuguese"], label="Select a model", value=language_to_models["Portuguese"][0])
+    language_radio.change(update_model_dropdown, inputs=language_radio, outputs=model_dropdown)
 
     with gr.Tabs():
         with gr.TabItem("Please input your text"):
-            input_text = gr.Textbox(
-                label="Input text",
-                info="Your text",
-                lines=3,
-                placeholder="Please input your text here",
-            )
-
-            input_sid = gr.Textbox(
-                label="Speaker ID",
-                info="Speaker ID",
-                lines=1,
-                max_lines=1,
-                value="0",
-                placeholder="Speaker ID. Valid only for mult-speaker model",
-            )
-
-            input_speed = gr.Slider(
-                minimum=0.1,
-                maximum=10,
-                value=1,
-                step=0.1,
-                label="Speed (larger->faster; smaller->slower)",
-            )
-
+            input_text = gr.Textbox(label="Input text", info="Your text", lines=3, placeholder="Please input your text here")
+            input_sid = gr.Textbox(label="Speaker ID", info="Speaker ID", lines=1, max_lines=1, value="0", placeholder="Speaker ID. Valid only for mult-speaker model")
+            input_speed = gr.Slider(minimum=0.1, maximum=10, value=1, step=0.1, label="Speed (larger->faster; smaller->slower)")
             input_button = gr.Button("Submit")
-
             output_audio = gr.Audio(label="Output")
-
             output_info = gr.HTML(label="Info")
+            gr.Examples(examples=examples, fn=process, inputs=[language_radio, model_dropdown, input_text, input_sid, input_speed], outputs=[output_audio, output_info])
 
-            gr.Examples(
-                examples=examples,
-                fn=process,
-                inputs=[
-                    language_radio,
-                    model_dropdown,
-                    input_text,
-                    input_sid,
-                    input_speed,
-                ],
-                outputs=[
-                    output_audio,
-                    output_info,
-                ],
-            )
-
-        input_button.click(
-            process,
-            inputs=[
-                language_radio,
-                model_dropdown,
-                input_text,
-                input_sid,
-                input_speed,
-            ],
-            outputs=[
-                output_audio,
-                output_info,
-            ],
-        )
+        input_button.click(process, inputs=[language_radio, model_dropdown, input_text, input_sid, input_speed], outputs=[output_audio, output_info])
 
     gr.Markdown(description)
 
-
 def download_espeak_ng_data():
-    os.system(
-        """
-    cd /tmp
-    wget -qq https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/espeak-ng-data.tar.bz2
-    tar xf espeak-ng-data.tar.bz2
-    """
-    )
-
+    os.system("""cd /tmp; wget -qq https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/espeak-ng-data.tar.bz2; tar xf espeak-ng-data.tar.bz2""")
 
 if __name__ == "__main__":
     download_espeak_ng_data()
     formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-
     logging.basicConfig(format=formatter, level=logging.INFO)
-
     demo.launch()