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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 numpy as np |
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from pathlib import Path |
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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.strip()] = name.strip() |
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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(audio_data=None, lang="eng (English)"): |
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assert lang.startswith("eng") |
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if not audio_data: |
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return "<<ERROR: Empty Audio Input>>" |
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if isinstance(audio_data, tuple): |
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sr, audio_samples = audio_data |
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audio_samples = (audio_samples / 32768.0).astype(np.float32) |
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if sr != ASR_SAMPLING_RATE: |
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audio_samples = librosa.resample( |
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audio_samples, orig_sr=sr, target_sr=ASR_SAMPLING_RATE |
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) |
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else: |
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if not isinstance(audio_data, str): |
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return "<<ERROR: Invalid Audio Input Instance: {}>>".format(type(audio_data)) |
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audio_samples = librosa.load(audio_data, 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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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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