Spaces:
Sleeping
Sleeping
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 = [ | |
["assets/english.mp3"], | |
["assets/tamil.mp3"], | |
["assets/burmese.mp3"], | |
] |