whisper-25TPS-VQ-32k-large-v3-turbo

25
1
—
by
malaysia-ai
Audio Model
OTHER
New
25 downloads
Early-stage
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Quick Summary

AI model with specialized capabilities.

Code Examples

how to audio tokenpythontransformers
from transformers import AutoFeatureExtractor, AutoModel, AutoTokenizer
import librosa

model_id = "mesolitica/whisper-25TPS-VQ-32k-large-v3-turbo"
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
model = AutoModel.from_pretrained(model_id, trust_remote_code = True, torch_dtype = 'auto').cuda()
encoder = model.model.get_encoder()
y, sr = librosa.load('common_voice_ba_26517811.mp3', sr = feature_extractor.sampling_rate)
features = feature_extractor([y], return_tensors = 'pt', return_attention_mask = True)
for k in features.keys():
    features[k] = features[k].cuda()
encoded = encoder(**features)
print(encoded[1][0, encoded[2][0] == 1])
how to audio tokenpythontransformers
from transformers import AutoFeatureExtractor, AutoModel, AutoTokenizer
import librosa

model_id = "mesolitica/whisper-25TPS-VQ-32k-large-v3-turbo"
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
model = AutoModel.from_pretrained(model_id, trust_remote_code = True, torch_dtype = 'auto').cuda()
encoder = model.model.get_encoder()
y, sr = librosa.load('common_voice_ba_26517811.mp3', sr = feature_extractor.sampling_rate)
features = feature_extractor([y], return_tensors = 'pt', return_attention_mask = True)
for k in features.keys():
    features[k] = features[k].cuda()
encoded = encoder(**features)
print(encoded[1][0, encoded[2][0] == 1])

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