Update app.py

app.py

@@ -1,24 +1,257 @@
+import os
+import time
+import uuid
+from datetime import datetime
+
 import gradio as gr
+import soundfile as sf
 from huggingface_hub import InferenceClient
 
+from model import get_pretrained_model, language_to_models
+
+
+def MyPrint(s):
+    now = datetime.now()
+    date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f")
+    print(f"{date_time}: {s}")
+
+
+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>
+"""
+
+# css style is copied from
+# https://huggingface.co/spaces/alphacep/asr/blob/main/app.py#L113
+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}
+"""
+
+examples = [
+    [
+        "Chinese (Mandarin, 普通话)",
+        "csukuangfj/matcha-icefall-zh-baker|1 speaker",
+        "某某银行的副行长和一些行政领导表示，他们去过长江和长白山; 经济不断增长。2024年12月31号，拨打110或者18920240511。123456块钱。",
+        0,
+        1.0,
+    ],
+    [
+        "Chinese (Mandarin, 普通话)",
+        "csukuangfj/vits-zh-hf-fanchen-wnj|1 speaker",
+        "在一个阳光明媚的夏天，小马、小羊和小狗它们一块儿在广阔的草地上，嬉戏玩耍，这时小猴来了，还带着它心爱的足球活蹦乱跳地跑前、跑后教小马、小羊、小狗踢足球。",
+        0,
+        1.0,
+    ],
+    [
+        "Chinese (Mandarin, 普通话)",
+        "csukuangfj/vits-zh-hf-fanchen-C|187 speakers",
+        '小米的使命是，始终坚持做"感动人心、价格厚道"的好产品，让全球每个人都能享受科技带来的美好生活。',
+        0,
+        1.0,
+    ],
+    ["Min-nan (闽南话)", "csukuangfj/vits-mms-nan", "ài piaǸ chiah ē iaN̂", 0, 1.0],
+    ["Thai", "csukuangfj/vits-mms-tha", "ฉันรักคุณ", 0, 1.0],
+    [
+        "Chinese (Mandarin, 普通话)",
+        "csukuangfj/sherpa-onnx-vits-zh-ll|5 speakers",
+        "当夜幕降临，星光点点，伴随着微风拂面，我在静谧中感受着时光的流转，思念如涟漪荡漾，梦境如画卷展开，我与自然融为一体，沉静在这片宁静的美丽之中，感受着生命的奇迹与温柔。",
+        2,
+        1.0,
+    ],
+]
+
+
+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 process(language: str, repo_id: str, text: str, sid: str, speed: float):
+    MyPrint(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."
+        )
+
+    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/>
+    """
+
+    MyPrint(info)
+    MyPrint(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",
+    )
+
+    return filename, build_html_output(info)
+
+
+# Démo Next-gen Kaldi TTS
+with gr.Blocks(css=css) as demo_tts:
+    gr.Markdown(title)
+    language_choices = list(language_to_models.keys())
+
+    language_radio = gr.Radio(
+        label="Language",
+        choices=language_choices,
+        value=language_choices[0],
+    )
+
+    model_dropdown = gr.Dropdown(
+        choices=language_to_models[language_choices[0]],
+        label="Select a model",
+        value=language_to_models[language_choices[0]][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_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,
+                ],
+            )
+
+        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
+    """
+    )
+
+
+# --- Deuxième script : Chat via InferenceClient ---
+
 client = InferenceClient("google/gemma-1.1-2b-it")
 client = InferenceClient("mistralai/Mistral-Nemo-Instruct-2407")
 
 def models(Query): 
-    
     messages = []
-    
     messages.append({"role": "user", "content": f"[SYSTEM] You are ASSISTANT who answer question asked by user in short and concise manner. [USER] {Query}"})
-
     Response = ""
-
     for message in client.chat_completion(
         messages,
         max_tokens=2048,
         stream=True
     ):
         token = message.choices[0].delta.content
-
         Response += token
         yield Response
 
@@ -53,23 +286,32 @@ Example format:
 <answer> [Final Answer] </answer> (must give final answer in this format)
 <reflection> [Evaluation of the solution] </reflection>
 <reward> [Float between 0.0 and 1.0] </reward> [/INST] [INST] [QUERY] {query} [/INST] [ASSISTANT] """
-
     stream = client.text_generation(message, max_new_tokens=4096, stream=True, details=True, return_full_text=False)
     output = ""
-
     for response in stream:
         output += response.token.text
     return output
 
-description="# Light ChatBox\n### Enter a question and.. Tada this reponse generate in 0.5 second!"
+description_chat = "# Light ChatBox\n### Enter a question and.. Tada this reponse generate in 0.5 second!"
 
 with gr.Blocks() as demo1:
-    gr.Interface(description=description,fn=models, inputs=["text"], outputs="text")
+    gr.Interface(description=description_chat, fn=models, inputs=["text"], outputs="text")
+
 with gr.Blocks() as demo2:
-    gr.Interface(description="Very low but critical thinker",fn=nemo, inputs=["text"], outputs="text", api_name="critical_thinker", concurrency_limit=10)
+    gr.Interface(description="Very low but critical thinker", fn=nemo, inputs=["text"], outputs="text", api_name="critical_thinker", concurrency_limit=10)
+
+with gr.Blocks() as demo_chat:
+    gr.TabbedInterface([demo1, demo2], ["Fast", "Critical"])
+
+
+# --- Interface globale combinée ---
+with gr.Blocks() as demo_combined:
+    gr.Markdown("# Application combinée")
+    gr.TabbedInterface([demo_tts, demo_chat], ["TTS", "Chat"])
+
+demo_combined.queue(max_size=300000)
 
-with gr.Blocks() as demo:
-    gr.TabbedInterface([demo1, demo2] , ["Fast", "Critical"])
 
-demo.queue(max_size=300000)
-demo.launch()
+if __name__ == "__main__":
+    download_espeak_ng_data()
+    demo_combined.launch()