app.py

import gradio as gr
import sys
import logging
from huggingsound import SpeechRecognitionModel
from transformers import pipeline, AutoModelForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM

# COPYPASTED FROM: https://huggingface.co/spaces/jonatasgrosman/asr/blob/main/app.py


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)

model_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-russian"
CACHED_MODEL = {"rus": AutoModelForCTC.from_pretrained(model_ID)}

def run(input_file, history, model_size="300M"):
    language = "Russian"
    decoding_type = "LM"
    logger.info(f"Running ASR {language}-{model_size}-{decoding_type} for {input_file}")

    # history = history or []
    # the history seems to be not by session anymore, so I'll deactivate this for now
    history = [] 

    model_instance = CACHED_MODEL.get("rus")
    if decoding_type == "LM":
        processor = Wav2Vec2ProcessorWithLM.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-russian")
        asr = pipeline("automatic-speech-recognition", model=model_instance, tokenizer=processor.tokenizer, 
                       feature_extractor=processor.feature_extractor, decoder=processor.decoder)
    else:
        processor = Wav2Vec2Processor.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-russian")
        asr = pipeline("automatic-speech-recognition", model=model_instance, tokenizer=processor.tokenizer, 
                       feature_extractor=processor.feature_extractor, decoder=None)

    transcription = asr(input_file.name, chunk_length_s=5, stride_length_s=1)["text"]
    
    logger.info(f"Transcription for {language}-{model_size}-{decoding_type} for {input_file}: {transcription}")

    history.append({
        "model_id": 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="file", label="Record something..."),
        "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)