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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):
if isinstance(audio_data, tuple):
# microphone
sr, audio_samples = audio_data
audio_samples = (audio_samples / 32768.0).astype(np.float32)
assert sr == LID_SAMPLING_RATE, "Invalid 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 = [
["./assets/english.mp3"],
["./assets/tamil.mp3"],
["./assets/burmese.mp3"],
] |