File size: 3,429 Bytes
b0744ce
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8582056
b0744ce
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8582056
b0744ce
 
 
 
7bcf2b3
 
b0744ce
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
import gradio as gr
import torch
from omegaconf import OmegaConf
from transformers import pipeline

device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32


def load_pipe(model_id: str):
    return pipeline(
        "automatic-speech-recognition",
        model=model_id,
        max_new_tokens=128,
        chunk_length_s=30,
        batch_size=8,
        torch_dtype=torch_dtype,
        device=device,
    )

OmegaConf.register_new_resolver("load_pipe", load_pipe)

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

def automatic_speech_recognition(model_id: str, dialect_id: str, audio_file: str):
    model = models_config[model_id]["model"]
    generate_kwargs = {
        "task": "transcribe",
        "language": "id",
        "num_beams": 1,
        "prompt_ids": torch.from_numpy(model.tokenizer.get_prompt_ids(dialect_id)).to(
            device
        ),
    }
    return model(audio_file, generate_kwargs=generate_kwargs)["text"].replace(f" {dialect_id}", "")


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

    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],
    )


demo = gr.Blocks(
    title="臺灣南島語語音辨識系統",
    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.Radio(
        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="族別",
    )

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

    gr.Markdown(
        """
        # 臺灣南島語語音辨識系統
        ### Formosan Automatic-Speech-Recognition System
        ### 研發團隊
        - **[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))**
        ### 合作單位
        - **[意傳科技](https://ithuan.tw/)、[原住民族語言研究發展基金會](https://www.ilrdf.org.tw/)**
        """
    )
    gr.Interface(
        automatic_speech_recognition,
        inputs=[
            model_drop_down,
            dialect_drop_down,
            gr.Audio(
                label="上傳或錄音",
                type="filepath",
                waveform_options=gr.WaveformOptions(
                    sample_rate=16000,
                ),
            ),
        ],
        outputs=[
            gr.Text(interactive=False, label="辨識結果"),
        ],
        allow_flagging="auto",
    )

demo.launch()