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import librosa |
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from transformers import Wav2Vec2ForCTC, AutoProcessor |
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import torch |
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import json |
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from huggingface_hub import hf_hub_download |
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from torchaudio.models.decoder import ctc_decoder |
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ASR_SAMPLING_RATE = 16_000 |
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ASR_LANGUAGES = {} |
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with open(f"data/asr/all_langs.tsv") as f: |
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for line in f: |
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iso, name = line.split(" ", 1) |
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ASR_LANGUAGES[iso] = name |
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MODEL_ID = "facebook/mms-1b-all" |
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processor = AutoProcessor.from_pretrained(MODEL_ID) |
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model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) |
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lm_decoding_config = {} |
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lm_decoding_configfile = hf_hub_download( |
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repo_id="facebook/mms-cclms", |
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filename="decoding_config.json", |
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subfolder="mms-1b-all", |
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) |
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with open(lm_decoding_configfile) as f: |
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lm_decoding_config = json.loads(f.read()) |
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decoding_config = lm_decoding_config["eng"] |
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lm_file = hf_hub_download( |
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repo_id="facebook/mms-cclms", |
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filename=decoding_config["lmfile"].rsplit("/", 1)[1], |
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subfolder=decoding_config["lmfile"].rsplit("/", 1)[0], |
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) |
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token_file = hf_hub_download( |
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repo_id="facebook/mms-cclms", |
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filename=decoding_config["tokensfile"].rsplit("/", 1)[1], |
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subfolder=decoding_config["tokensfile"].rsplit("/", 1)[0], |
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) |
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lexicon_file = None |
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if decoding_config["lexiconfile"] is not None: |
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lexicon_file = hf_hub_download( |
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repo_id="facebook/mms-cclms", |
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filename=decoding_config["lexiconfile"].rsplit("/", 1)[1], |
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subfolder=decoding_config["lexiconfile"].rsplit("/", 1)[0], |
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) |
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beam_search_decoder = ctc_decoder( |
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lexicon=lexicon_file, |
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tokens=token_file, |
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lm=lm_file, |
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nbest=1, |
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beam_size=500, |
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beam_size_token=50, |
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lm_weight=float(decoding_config["lmweight"]), |
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word_score=float(decoding_config["wordscore"]), |
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sil_score=float(decoding_config["silweight"]), |
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blank_token="<s>", |
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) |
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def transcribe( |
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audio_source=None, microphone=None, file_upload=None, lang="eng (English)" |
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): |
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if type(microphone) is dict: |
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microphone = microphone["name"] |
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audio_fp = ( |
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file_upload if "upload" in str(audio_source or "").lower() else microphone |
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) |
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if audio_fp is None: |
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return "ERROR: You have to either use the microphone or upload an audio file" |
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audio_samples = librosa.load(audio_fp, sr=ASR_SAMPLING_RATE, mono=True)[0] |
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lang_code = lang.split()[0] |
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processor.tokenizer.set_target_lang(lang_code) |
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model.load_adapter(lang_code) |
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inputs = processor( |
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audio_samples, sampling_rate=ASR_SAMPLING_RATE, return_tensors="pt" |
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) |
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if torch.cuda.is_available(): |
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device = torch.device("cuda") |
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elif ( |
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hasattr(torch.backends, "mps") |
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and torch.backends.mps.is_available() |
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and torch.backends.mps.is_built() |
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): |
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device = torch.device("mps") |
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else: |
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device = torch.device("cpu") |
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model.to(device) |
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inputs = inputs.to(device) |
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with torch.no_grad(): |
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outputs = model(**inputs).logits |
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if lang_code != "eng": |
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ids = torch.argmax(outputs, dim=-1)[0] |
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transcription = processor.decode(ids) |
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else: |
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beam_search_result = beam_search_decoder(outputs.to("cpu")) |
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transcription = " ".join(beam_search_result[0][0].words).strip() |
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return transcription |
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ASR_EXAMPLES = [ |
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[None, "assets/english.mp3", None, "eng (English)"], |
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] |
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ASR_NOTE = """ |
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The above demo uses beam-search decoding with LM for English and greedy decoding results for all other languages. |
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Checkout the instructions [here](https://huggingface.co/facebook/mms-1b-all) on how to run LM decoding for other languages. |
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""" |
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