app.py

import json

import gradio as gr
from omegaconf import OmegaConf
from huggingface_hub import snapshot_download
from vosk import Model, KaldiRecognizer

def load_vosk(model_id: str):
    model_dir = snapshot_download(model_id)
    return Model(model_path=model_dir)

OmegaConf.register_new_resolver("load_vosk", load_vosk)

models_config = OmegaConf.to_object(OmegaConf.load("configs/models.yaml"))

def automatic_speech_recognition(model_id: str, dialect_id: str, audio_data: str):
    if isinstance(models_config[model_id]["model"], dict):
        model = models_config[model_id]["model"][dialect_id]
    else: 
        model = models_config[model_id]["model"]
        
    sample_rate, audio_array = audio_data
    if audio_array.ndim == 2:
        audio_array = audio_array[:, 0]

    audio_bytes = audio_array.tobytes()

    rec = KaldiRecognizer(model, sample_rate)

    rec.SetWords(True)

    results = []
    
    for start in range(0, len(audio_bytes), 4000):
        end = min(start + 4000, len(audio_bytes))
        data = audio_bytes[start:end]
        if rec.AcceptWaveform(data):
            raw_result = json.loads(rec.Result())
            results.append(raw_result)
                
    final_result = json.loads(rec.FinalResult())
    results.append(final_result)

    filtered_lines = []

    for result in results:
        result["text"] = result["text"].replace(" ", "")
        if len(result["text"]) > 0:
            filtered_lines.append(result["text"])

  
    return "，".join(filtered_lines) + "。"


def when_model_selected(model_id: str):
    model_config = models_config[model_id]

    if "dialect_mapping" not in model_config:
        return gr.update(visible=False)

    dialect_drop_down_choices = [
        (k, v) for k, v in model_config["dialect_mapping"].items()
    ]

    return gr.update(
        choices=dialect_drop_down_choices,
        value=dialect_drop_down_choices[0][1],
        visible=True,
    )


demo = gr.Blocks(
    title="臺灣客語語音辨識系統",
    css="@import url(https://tauhu.tw/tauhu-oo.css);",
    theme=gr.themes.Default(
        font=(
            "tauhu-oo",
            gr.themes.GoogleFont("Source Sans Pro"),
            "ui-sans-serif",
            "system-ui",
            "sans-serif",
        )
    ),
)

with demo:
    default_model_id = list(models_config.keys())[0]
    model_drop_down = gr.Dropdown(
        models_config.keys(),
        value=default_model_id,
        label="模型",
    )

    dialect_drop_down = gr.Dropdown(
        choices=[
            (k, v)
            for k, v in models_config[default_model_id]["dialect_mapping"].items()
        ],
        value=list(models_config[default_model_id]["dialect_mapping"].values())[0],
        label="腔調",
    )

    model_drop_down.input(
        when_model_selected,
        inputs=[model_drop_down],
        outputs=[dialect_drop_down],
    )

    gr.Markdown(
        """
        # 臺灣客語語音辨識系統
        ### Taiwanese Hakka Automatic-Speech-Recognition System
        ### 研發
        - **[李鴻欣 Hung-Shin Lee](mailto:hungshinlee@gmail.com)（[聯和科創](https://www.104.com.tw/company/1a2x6bmu75)）**
        - **[陳力瑋 Li-Wei Chen](mailto:wayne900619@gmail.com)（[聯和科創](https://www.104.com.tw/company/1a2x6bmu75)）**
        """
    )
    gr.Interface(
        automatic_speech_recognition,
        inputs=[
            model_drop_down,
            dialect_drop_down,
            gr.Audio(
                label="上傳或錄音",
                type="numpy",
                format="wav",
                waveform_options=gr.WaveformOptions(
                    sample_rate=16000,
                ),
            ),
        ],
        outputs=[
            gr.Text(interactive=False, label="客語漢字"),
        ],
        allow_flagging="auto",
    )

demo.launch()