Image Feature Extraction
Transformers
Safetensors
moss-audio-tokenizer
audio
audio-tokenizer
neural-codec
moss-tts-family
MOSS Audio Tokenizer
speech-tokenizer
trust-remote-code
custom_code
Instructions to use OpenMOSS-Team/MOSS-Audio-Tokenizer-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMOSS-Team/MOSS-Audio-Tokenizer-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="OpenMOSS-Team/MOSS-Audio-Tokenizer-v2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenMOSS-Team/MOSS-Audio-Tokenizer-v2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload MOSS Audio Tokenizer v2
Browse files
modeling_moss_audio_tokenizer.py
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@@ -2469,7 +2469,7 @@ class MossAudioTokenizerModel(MossAudioTokenizerPreTrainedModel):
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>>> import torch
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>>> from transformers import MossAudioTokenizerModel
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>>> model = MossAudioTokenizerModel.from_pretrained("
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>>> # Create dummy audio input
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>>> audio = torch.randn(1, 1, 24000) # 1 second of audio at 24kHz
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>>> import torch
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>>> from transformers import MossAudioTokenizerModel
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>>> model = MossAudioTokenizerModel.from_pretrained("OpenMOSS-Team/MOSS-Audio-Tokenizer-v2/")
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>>> # Create dummy audio input
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>>> audio = torch.randn(1, 1, 24000) # 1 second of audio at 24kHz
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