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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],
)
with open("DEMO.md") as tong:
gr.Markdown(tong.read())
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()
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