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vineelpratap
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69b07b9
Update asr_lm_eng.py
Browse files- asr_lm_eng.py +48 -63
asr_lm_eng.py
CHANGED
@@ -21,54 +21,56 @@ processor = AutoProcessor.from_pretrained(MODEL_ID)
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model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
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#
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def transcribe(audio_data=None, lang="eng (English)"):
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if not audio_data:
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return "<<ERROR: Empty Audio Input>>"
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@@ -113,24 +115,7 @@ def transcribe(audio_data=None, lang="eng (English)"):
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with torch.no_grad():
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outputs = model(**inputs).logits
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transcription = processor.decode(ids)
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else:
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assert False
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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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["upload/english.mp3", "eng (English)"],
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# ["upload/tamil.mp3", "tam (Tamil)"],
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# ["upload/burmese.mp3", "mya (Burmese)"],
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]
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ASR_NOTE = """
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The above demo doesn't use beam-search decoding using a language model.
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Checkout the instructions [here](https://huggingface.co/facebook/mms-1b-all) on how to run LM decoding for better accuracy.
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"""
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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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# allow language model decoding for "eng"
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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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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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