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import logging
import sys
import gradio as gr
import vosk
import json
import subprocess
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
LARGE_MODEL_BY_LANGUAGE = {
"Russian": {"model_id": "vosk-model-ru-0.42"},
"Chinese": {"model_id": "vosk-model-cn-0.22"},
"English": {"model_id": "vosk-model-en-us-0.22"},
"French": {"model_id": "vosk-model-fr-0.22"},
"German": {"model_id": "vosk-model-de-0.22"},
"Italian": {"model_id": "vosk-model-it-0.22"},
"Japanese": {"model_id": "vosk-model-ja-0.22"},
"Hindi": {"model_id": "vosk-model-hi-0.22"},
"Persian": {"model_id": "vosk-model-fa-0.5"},
"Uzbek": {"model_id": "vosk-model-small-uz-0.22"},
}
LANGUAGES = sorted(LARGE_MODEL_BY_LANGUAGE.keys())
CACHED_MODELS_BY_ID = {}
def asr(model, input_file):
rec = vosk.KaldiRecognizer(model, 16000.0)
results = []
process = subprocess.Popen(f'ffmpeg -loglevel quiet -i {input_file} -ar 16000 -ac 1 -f s16le -'.split(),
stdout=subprocess.PIPE)
while True:
data = process.stdout.read(4000)
if len(data) == 0:
break
if rec.AcceptWaveform(data):
jres = json.loads(rec.Result())
results.append(jres['text'])
jres = json.loads(rec.FinalResult())
results.append(jres['text'])
return " ".join(results)
def run(input_file, language, history):
logger.info(f"Running ASR for {language} for {input_file}")
history = history or []
model = LARGE_MODEL_BY_LANGUAGE.get(language, None)
if model is None:
history.append({
"error_message": f"Failed to find a model for {language} language :("
})
elif input_file is None:
history.append({
"error_message": f"Record input audio first"
})
else:
model_instance = CACHED_MODELS_BY_ID.get(model["model_id"], None)
if model_instance is None:
model_instance = vosk.Model(model_name=model["model_id"])
CACHED_MODELS_BY_ID[model["model_id"]] = model_instance
transcription = asr(model_instance, input_file)
logger.info(f"Transcription for {input_file}: {transcription}")
history.append({
"model_id": model["model_id"],
"language": language,
"transcription": transcription,
"error_message": None
})
html_output = "<div class='result'>"
for item in history:
if item["error_message"] is not None:
html_output += f"<div class='result_item result_item_error'>{item['error_message']}</div>"
else:
html_output += "<div class='result_item result_item_success'>"
html_output += f'{item["transcription"]}<br/>'
html_output += "</div>"
html_output += "</div>"
return html_output, history
gr.Interface(
run,
inputs=[
gr.inputs.Audio(source="microphone", type="filepath", label="Record something..."),
gr.inputs.Radio(label="Language", choices=LANGUAGES),
"state"
],
outputs=[
gr.outputs.HTML(label="Outputs"),
"state"
],
title="Automatic Speech Recognition",
description="",
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}
""",
allow_flagging="never",
theme="default"
).launch(enable_queue=True)
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