Delete lid.py(auto/audio)

lid.py(auto/audio)

@@ -1,69 +0,0 @@
-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 detect_language(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()}
-