Create lid.py

lid.py

@@ -0,0 +1,75 @@
+from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor
+import torch
+import librosa
+import numpy as np
+
+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_data = None):
+    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 != LID_SAMPLING_RATE:
+            audio_samples = librosa.resample(
+                audio_samples, orig_sr=sr, target_sr=LID_SAMPLING_RATE
+            )
+    else:
+        # file upload
+        isinstance(audio_data, str)
+        audio_samples = librosa.load(audio_data, 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 = [
+    ["upload/english.mp3"],
+    ["upload/tamil.mp3"],
+    ["upload/burmese.mp3"],
+]