Create lid.py

lid.py

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+from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor
+import torch
+import librosa
+
+model_id = "facebook/mms-lid-1024"
+
+processor = AutoFeatureExtractor.from_pretrained(model_id)
+model = Wav2Vec2ForSequenceClassification.from_pretrained(model_id)
+
+
+LID_SAMPLING_RATE = 16_000
+LID_TOPK = 10
+LID_THRESHOLD = 0.33
+
+LID_LANGUAGES = {}
+with open(f"data/lid/all_langs.tsv") as f:
+    for line in f:
+        iso, name = line.split(" ", 1)
+        LID_LANGUAGES[iso] = name
+
+
+def identify(audio_source=None, microphone=None, file_upload=None):
+    if audio_source is None and microphone is None and file_upload is None:
+        # HACK: need to handle this case for some reason
+        return {}
+
+    if type(microphone) is dict:
+        # HACK: microphone variable is a dict when running on examples
+        microphone = microphone["name"]
+    audio_fp = (
+        file_upload if "upload" in str(audio_source or "").lower() else microphone
+    )
+    if audio_fp is None:
+        return "ERROR: You have to either use the microphone or upload an audio file"
+    
+    audio_samples = librosa.load(audio_fp, sr=LID_SAMPLING_RATE, mono=True)[0]
+
+    inputs = processor(
+        audio_samples, sampling_rate=LID_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():
+        logit = model(**inputs).logits
+
+    logit_lsm = torch.log_softmax(logit.squeeze(), dim=-1)
+    scores, indices = torch.topk(logit_lsm, 5, dim=-1)
+    scores, indices = torch.exp(scores).to("cpu").tolist(), indices.to("cpu").tolist()
+    iso2score = {model.config.id2label[int(i)]: s for s, i in zip(scores, indices)}
+    if max(iso2score.values()) < LID_THRESHOLD:
+        return "Low confidence in the language identification predictions. Output is not shown!"
+    return {LID_LANGUAGES[iso]: score for iso, score in iso2score.items()}
+
+
+LID_EXAMPLES = [
+    [None, "./assets/english.mp3", None],
+    [None, "./assets/tamil.mp3", None],
+    [None, "./assets/burmese.mp3", None],
+]