import librosa from transformers import Wav2Vec2ForCTC, AutoProcessor import torch 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="", # ) 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) assert sr == ASR_SAMPLING_RATE, "Invalid sampling rate" else: # file upload isinstance(audio_data, str) audio_samples = librosa.load(audio_data, sr=ASR_SAMPLING_RATE, mono=True)[0] 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. """