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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_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.strip()
def identify_language(audio=None):
if audio is None:
return "ERROR: You have to either use the microphone or upload an audio file"
audio_samples = librosa.load(audio, sr=LID_SAMPLING_RATE, mono=True)[0]
inputs = processor(audio_samples, sampling_rate=LID_SAMPLING_RATE, return_tensors="pt")
device = torch.device("cuda" if torch.cuda.is_available() else "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()}
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