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| import numpy as np | |
| from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC | |
| import torch | |
| import torchaudio | |
| from umsc import UgMultiScriptConverter | |
| import util | |
| # Model ID and setup | |
| model_id = 'ixxan/wav2vec2-large-mms-1b-uyghur-latin' | |
| asr_model = Wav2Vec2ForCTC.from_pretrained(model_id, target_lang="uig-script_latin") | |
| asr_processor = Wav2Vec2Processor.from_pretrained(model_id) | |
| asr_processor.tokenizer.set_target_lang("uig-script_latin") | |
| # Automatically allocate the device | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| asr_model = asr_model.to(device) | |
| def asr(audio_data, target_rate = 16000): | |
| # Load and resample user audio | |
| if isinstance(audio_data, tuple): | |
| # microphone | |
| sampling_rate, audio_input = audio_data | |
| audio_input = (audio_input / 32768.0).astype(np.float32) | |
| elif isinstance(audio_data, str): | |
| # file upload | |
| audio_input, sampling_rate = torchaudio.load(audio_data) | |
| else: | |
| return "<<ERROR: Invalid Audio Input Instance: {}>>".format(type(audio_data)) | |
| # Resample if needed | |
| if sampling_rate != target_rate: | |
| resampler = torchaudio.transforms.Resample(sampling_rate, target_rate) | |
| audio_input = resampler(audio_input) | |
| # Process audio through ASR model | |
| inputs = asr_processor(audio_input.squeeze(), sampling_rate=sampling_rate, return_tensors="pt", padding=True) | |
| inputs = {key: val.to(device) for key, val in inputs.items()} | |
| with torch.no_grad(): | |
| logits = asr_model(**inputs).logits | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| transcript = asr_processor.batch_decode(predicted_ids)[0] | |
| return transcript | |
| def check_pronunciation(input_text, script, user_audio): | |
| # Transcripts from user input audio | |
| transcript_ugLatn_box = asr(user_audio) | |
| ug_latn_to_arab = UgMultiScriptConverter('ULS', 'UAS') | |
| transcript_ugArab_box = ug_latn_to_arab(transcript_ugLatn_box) | |
| # Get IPA and Pronunciation Feedback | |
| if script == 'Uyghur Latin': | |
| input_text = ug_latn_to_arab(input_text) # make sure input text is arabic script to IPA conversion | |
| machine_pronunciation, user_pronunciation, pronunciation_match, pronunciation_score = util.calculate_pronunciation_accuracy( | |
| reference_text = input_text, | |
| output_text = transcript_ugArab_box, | |
| language_code='uig-Arab') | |
| return transcript_ugArab_box, transcript_ugLatn_box, machine_pronunciation, user_pronunciation, pronunciation_match, pronunciation_score |