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| import librosa | |
| from transformers import Wav2Vec2ForCTC, AutoProcessor | |
| import torch | |
| import json | |
| import numpy as np | |
| from huggingface_hub import hf_hub_download | |
| from torchaudio.models.decoder import ctc_decoder | |
| ASR_SAMPLING_RATE = 16_000 | |
| ASR_LANGUAGES = {} | |
| with open(f"data/asr/all_langs.tsv") as f: | |
| for line in f: | |
| iso, name = line.split(" ", 1) | |
| ASR_LANGUAGES[iso] = name | |
| MODEL_ID = "facebook/mms-1b-all" | |
| processor = AutoProcessor.from_pretrained(MODEL_ID) | |
| model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) | |
| # lm_decoding_config = {} | |
| # lm_decoding_configfile = hf_hub_download( | |
| # repo_id="facebook/mms-cclms", | |
| # filename="decoding_config.json", | |
| # subfolder="mms-1b-all", | |
| # ) | |
| # with open(lm_decoding_configfile) as f: | |
| # lm_decoding_config = json.loads(f.read()) | |
| # # allow language model decoding for "eng" | |
| # decoding_config = lm_decoding_config["eng"] | |
| # lm_file = hf_hub_download( | |
| # repo_id="facebook/mms-cclms", | |
| # filename=decoding_config["lmfile"].rsplit("/", 1)[1], | |
| # subfolder=decoding_config["lmfile"].rsplit("/", 1)[0], | |
| # ) | |
| # token_file = hf_hub_download( | |
| # repo_id="facebook/mms-cclms", | |
| # filename=decoding_config["tokensfile"].rsplit("/", 1)[1], | |
| # subfolder=decoding_config["tokensfile"].rsplit("/", 1)[0], | |
| # ) | |
| # lexicon_file = None | |
| # if decoding_config["lexiconfile"] is not None: | |
| # lexicon_file = hf_hub_download( | |
| # repo_id="facebook/mms-cclms", | |
| # filename=decoding_config["lexiconfile"].rsplit("/", 1)[1], | |
| # subfolder=decoding_config["lexiconfile"].rsplit("/", 1)[0], | |
| # ) | |
| # beam_search_decoder = ctc_decoder( | |
| # lexicon=lexicon_file, | |
| # tokens=token_file, | |
| # lm=lm_file, | |
| # nbest=1, | |
| # beam_size=500, | |
| # beam_size_token=50, | |
| # lm_weight=float(decoding_config["lmweight"]), | |
| # word_score=float(decoding_config["wordscore"]), | |
| # sil_score=float(decoding_config["silweight"]), | |
| # blank_token="<s>", | |
| # ) | |
| def transcribe(audio_data, lang="eng (English)"): | |
| if isinstance(audio_data, tuple): | |
| # microphone | |
| sr, audio_samples = audio_data | |
| audio_samples = (audio_samples/32768.0).astype(np.float) | |
| print("case1", audio_samples[:5]) | |
| assert sr == ASR_SAMPLING_RATE, "Invalid sampling rate" | |
| else: | |
| # file upload | |
| isinstance(audio_data, str) | |
| print("case2 1", audio_data) | |
| audio_samples = librosa.load(audio_fp, sr=ASR_SAMPLING_RATE, mono=True)[0] | |
| print("case2", audio_samples[:5]) | |
| lang_code = lang.split()[0] | |
| processor.tokenizer.set_target_lang(lang_code) | |
| model.load_adapter(lang_code) | |
| inputs = processor( | |
| audio_samples, sampling_rate=ASR_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(): | |
| outputs = model(**inputs).logits | |
| if lang_code != "eng" or True: | |
| ids = torch.argmax(outputs, dim=-1)[0] | |
| transcription = processor.decode(ids) | |
| else: | |
| assert False | |
| # beam_search_result = beam_search_decoder(outputs.to("cpu")) | |
| # transcription = " ".join(beam_search_result[0][0].words).strip() | |
| return transcription | |
| ASR_EXAMPLES = [ | |
| ["assets/english.mp3", "eng (English)"], | |
| # ["assets/tamil.mp3", "tam (Tamil)"], | |
| # ["assets/burmese.mp3", "mya (Burmese)"], | |
| ] | |
| ASR_NOTE = """ | |
| The above demo doesn't use beam-search decoding using a language model. | |
| Checkout the instructions [here](https://huggingface.co/facebook/mms-1b-all) on how to run LM decoding for better accuracy. | |
| """ | |