Create model.py

model.py

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+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 = "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.
+
+"""
+
+# 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 = [
+#    ["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],
+#]
+
+
+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):
+    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."
+        )
+
+    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/>
+    """
+
+    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",
+    )
+
+    return filename, build_html_output(info)
+
+
+demo = gr.Blocks(css=css)
+
+
+with demo:
+    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
+    """
+    )
+
+
+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()