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A10G
Running
on
A10G
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_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_source=None, microphone=None, file_upload=None): | |
if audio_source is None and microphone is None and file_upload is None: | |
# HACK: need to handle this case for some reason | |
return {} | |
if type(microphone) is dict: | |
# HACK: microphone variable is a dict when running on examples | |
microphone = microphone["name"] | |
audio_fp = ( | |
file_upload if "upload" in str(audio_source or "").lower() else microphone | |
) | |
audio_samples = librosa.load(audio_fp, 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 = [ | |
[None, "./assets/english.mp3", None], | |
[None, "./assets/tamil.mp3", None], | |
[None, "./assets/burmese.mp3", None], | |
] | |