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| import gradio as gr | |
| from transformers import pipeline, AutoModelForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM | |
| MODELS = { | |
| "Tatar": {"model_id": "sammy786/wav2vec2-xlsr-tatar", "has_lm": False}, | |
| "Chuvash": {"model_id": "sammy786/wav2vec2-xlsr-chuvash", "has_lm": False} | |
| } | |
| CACHED_MODELS_BY_ID = {} | |
| LANGUAGES = sorted(MODELS.keys()) | |
| def run(input_file, language, decoding_type, history): | |
| #logger.info(f"Running ASR {language}-{model_size}-{decoding_type} for {input_file}") | |
| model = MODELS.get(language, None) | |
| if 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"] | |
| return transcription | |
| gr.Interface( | |
| run, | |
| inputs=[ | |
| gr.Audio(source="microphone", type="filepath", label="Record something..."), | |
| gr.Radio(label="Language", choices=LANGUAGES), | |
| gr.Radio(label="Decoding type", choices=["greedy", "LM"]), | |
| # gr.inputs.Radio(label="Model size", choices=["300M", "1B"]), | |
| "state" | |
| ], | |
| outputs=[ | |
| gr.TextBox | |
| ], | |
| allow_screenshot=False, | |
| allow_flagging="never", | |
| theme="grass" | |
| ).launch(enable_queue=True) |