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("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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
modify default attention backend to flash_attn
Browse files- config.json +2 -2
config.json
CHANGED
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@@ -13,8 +13,8 @@
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"causal_transformer_context_duration": 10.0,
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"number_channels": 2,
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"enable_channel_interleave": true,
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"attention_implementation": "
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"compute_dtype": "
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"dtype": "float32",
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"code_dim": 768,
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"encoder_kwargs": [
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"causal_transformer_context_duration": 10.0,
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"number_channels": 2,
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"enable_channel_interleave": true,
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+
"attention_implementation": "flash_attention_2",
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"compute_dtype": "bf16",
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"dtype": "float32",
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"code_dim": 768,
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"encoder_kwargs": [
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