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| 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) |