Create asr.py

asr.py

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+import librosa
+from transformers import Wav2Vec2ForCTC, AutoProcessor
+import torch
+import numpy as np
+from pathlib import Path
+
+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.strip()] = name.strip()
+
+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=None, lang="eng (English)"):
+
+    if not audio_data:
+        return "<<ERROR: Empty Audio Input>>"
+    
+    if isinstance(audio_data, tuple):
+        # microphone
+        sr, audio_samples = audio_data
+        audio_samples = (audio_samples / 32768.0).astype(np.float32)
+        if sr != ASR_SAMPLING_RATE:
+            audio_samples = librosa.resample(
+                audio_samples, orig_sr=sr, target_sr=ASR_SAMPLING_RATE
+            )
+    else:
+        # file upload
+        
+        if not isinstance(audio_data, str):
+            return "<<ERROR: Invalid Audio Input Instance: {}>>".format(type(audio_data))
+        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 = [
+    ["upload/english.mp3", "eng (English)"],
+    # ["upload/tamil.mp3", "tam (Tamil)"],
+    # ["upload/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.
+"""