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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) | |
| DICT_MODELS = { | |
| "robust-300m": {"model_id": "dbdmg/wav2vec2-xls-r-300m-italian-robust", "has_lm": True}, | |
| "robust-1b": {"model_id": "dbdmg/wav2vec2-xls-r-1b-italian-robust", "has_lm": True}, | |
| "300m": {"model_id": "dbdmg/wav2vec2-xls-r-300m-italian", "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 | |
| MODELS = sorted(DICT_MODELS.keys()) | |
| CACHED_MODELS_BY_ID = {} | |
| def build_html(history): | |
| 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 = " + Guided by Language Model" if item["decoding_type"] == "Guided by Language Model" 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 | |
| def run(uploaded_file, input_file, model_name, decoding_type, history): | |
| model = DICT_MODELS.get(model_name) | |
| history = history or [] | |
| if uploaded_file is None and input_file is None: | |
| history.append({ | |
| "model_id": model["model_id"], | |
| "decoding_type": decoding_type, | |
| "transcription": "", | |
| "error_message": "No input provided." | |
| }) | |
| else: | |
| if input_file is None: | |
| input_file = uploaded_file | |
| logger.info(f"Running ASR {model_name}-{decoding_type} for {input_file}") | |
| history = history or [] | |
| if model is None: | |
| history.append({ | |
| "error_message": f"Model size {model_size} not found for {language} language :(" | |
| }) | |
| elif decoding_type == "Guided by Language Model" 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 == "Guided by Language Model": | |
| 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"], | |
| "decoding_type": decoding_type, | |
| "transcription": transcription, | |
| "error_message": None | |
| }) | |
| html_output = build_html(history) | |
| return html_output, history | |
| gr.Interface( | |
| run, | |
| inputs=[ | |
| gr.inputs.Audio(source="upload", type='filepath', optional=True), | |
| gr.inputs.Audio(source="microphone", type="filepath", label="Record something...", optional=True), | |
| gr.inputs.Radio(label="Model", choices=MODELS), | |
| gr.inputs.Radio(label="Decoding type", choices=["Standard", "Guided by Language Model"]), | |
| "state" | |
| ], | |
| outputs=[ | |
| gr.outputs.HTML(label="Outputs"), | |
| "state" | |
| ], | |
| title="Italian Robust ASR", | |
| 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="huggingface", | |
| examples = [ | |
| ['demo_example_1.mp3', 'demo_example_1.mp3', 'robust-300m', 'Guided by Language Model'], | |
| ['demo_luca_1.wav', 'demo_luca_1.wav', 'robust-300m', 'Guided by Language Model'], | |
| ['demo_luca_2.wav', 'demo_luca_2.wav', 'robust-300m', 'Guided by Language Model'] | |
| ] | |
| ).launch(enable_queue=True) |