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| from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor | |
| import torch | |
| import librosa | |
| model_id = "facebook/mms-lid-1024" | |
| processor = AutoFeatureExtractor.from_pretrained(model_id) | |
| model = Wav2Vec2ForSequenceClassification.from_pretrained(model_id) | |
| LID_SAMPLING_RATE = 16_000 | |
| LID_TOPK = 10 | |
| LID_THRESHOLD = 0.33 | |
| LID_LANGUAGES = {} | |
| with open(f"data/lid/all_langs.tsv") as f: | |
| for line in f: | |
| iso, name = line.split(" ", 1) | |
| LID_LANGUAGES[iso] = name | |
| def identify(audio_source=None, microphone=None, file_upload=None): | |
| if audio_source is None and microphone is None and file_upload is None: | |
| # HACK: need to handle this case for some reason | |
| return {} | |
| if type(microphone) is dict: | |
| # HACK: microphone variable is a dict when running on examples | |
| microphone = microphone["name"] | |
| audio_fp = ( | |
| file_upload if "upload" in str(audio_source or "").lower() else microphone | |
| ) | |
| if audio_fp is None: | |
| return "ERROR: You have to either use the microphone or upload an audio file" | |
| audio_samples = librosa.load(audio_fp, sr=LID_SAMPLING_RATE, mono=True)[0] | |
| inputs = processor( | |
| audio_samples, sampling_rate=LID_SAMPLING_RATE, return_tensors="pt" | |
| ) | |
| # set device | |
| if torch.cuda.is_available(): | |
| device = torch.device("cuda") | |
| elif ( | |
| hasattr(torch.backends, "mps") | |
| and torch.backends.mps.is_available() | |
| and torch.backends.mps.is_built() | |
| ): | |
| device = torch.device("mps") | |
| else: | |
| device = torch.device("cpu") | |
| model.to(device) | |
| inputs = inputs.to(device) | |
| with torch.no_grad(): | |
| logit = model(**inputs).logits | |
| logit_lsm = torch.log_softmax(logit.squeeze(), dim=-1) | |
| scores, indices = torch.topk(logit_lsm, 5, dim=-1) | |
| scores, indices = torch.exp(scores).to("cpu").tolist(), indices.to("cpu").tolist() | |
| iso2score = {model.config.id2label[int(i)]: s for s, i in zip(scores, indices)} | |
| if max(iso2score.values()) < LID_THRESHOLD: | |
| return "Low confidence in the language identification predictions. Output is not shown!" | |
| return {LID_LANGUAGES[iso]: score for iso, score in iso2score.items()} | |
| LID_EXAMPLES = [ | |
| [None, "./assets/english.mp3", None], | |
| [None, "./assets/tamil.mp3", None], | |
| [None, "./assets/burmese.mp3", None], | |
| ] | |