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import logging
import sys
import gradio as gr
from transformers import pipeline, AutoModelForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM
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 = {
"Korean": {"model_id": "kresnik/wav2vec2-large-xlsr-korean", "has_lm": True},
}
# LANGUAGES = sorted(LARGE_MODEL_BY_LANGUAGE.keys())
# the container given by HF has 16GB of RAM, so we need to limit the number of models to load
LANGUAGES = sorted(LARGE_MODEL_BY_LANGUAGE.keys())
CACHED_MODELS_BY_ID = {}
def run(input_file, language, decoding_type, history, model_size="300M"):
logger.info(f"Running ASR {language}-{model_size}-{decoding_type} for {input_file}")
history = history or []
if model_size == "300M":
model = LARGE_MODEL_BY_LANGUAGE.get(language, None)
else:
model = XLARGE_MODEL_BY_LANGUAGE.get(language, None)
if model is None:
history.append({
"error_message": f"Model size {model_size} not found for {language} language :("
})
elif decoding_type == "LM" and not model["has_lm"]:
history.append({
"error_message": f"LM not available for {language} language :("
})
else:
# model_instance = AutoModelForCTC.from_pretrained(model["model_id"])
model_instance = CACHED_MODELS_BY_ID.get(model["model_id"], None)
if model_instance is None:
model_instance = AutoModelForCTC.from_pretrained(model["model_id"])
CACHED_MODELS_BY_ID[model["model_id"]] = model_instance
if decoding_type == "LM":
processor = Wav2Vec2ProcessorWithLM.from_pretrained(model["model_id"])
asr = pipeline("automatic-speech-recognition", model=model_instance, tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor, decoder=processor.decoder)
else:
processor = Wav2Vec2Processor.from_pretrained(model["model_id"])
asr = pipeline("automatic-speech-recognition", model=model_instance, tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor, decoder=None)
transcription = asr(input_file, chunk_length_s=5, stride_length_s=1)["text"]
logger.info(f"Transcription for {input_file}: {transcription}")
history.append({
"model_id": model["model_id"],
"language": language,
"model_size": model_size,
"decoding_type": decoding_type,
"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:
url_suffix = " + LM" if item["decoding_type"] == "LM" else ""
html_output += "<div class='result_item result_item_success'>"
html_output += f'<strong><a target="_blank" href="https://huggingface.co/{item["model_id"]}">{item["model_id"]}{url_suffix}</a></strong><br/><br/>'
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),
gr.inputs.Radio(label="Decoding type", choices=["greedy"]),
# gr.inputs.Radio(label="Model size", choices=["300M", "1B"]),
"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_screenshot=False,
allow_flagging="never",
theme="grass"
).launch(enable_queue=True)
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