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import json
import gradio as gr
from huggingface_hub import snapshot_download
from omegaconf import OmegaConf
from vosk import KaldiRecognizer, Model
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):
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="康統 Kaldi 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",
)
gr.Markdown(
"""
# 康統 Kaldi 語音辨識系統
"""
)
gr.Interface(
automatic_speech_recognition,
inputs=[
model_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="文字"),
],
allow_flagging="auto",
)
demo.launch()
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