Any-to-Any
ESPnet
PyTorch
English
audio
multimodal
speech-language-model
audio-understanding
audio-generation
text-to-audio
Instructions to use espnet/bagpiper-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ESPnet
How to use espnet/bagpiper-sft with ESPnet:
unknown model type (must be text-to-speech or automatic-speech-recognition)
- Notebooks
- Google Colab
- Kaggle
File size: 963 Bytes
df2e49a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | {
"artifact": "espnet/bagpiper-sft",
"format": "native ESPnet weight-only checkpoint",
"weight_file": {
"path": "model.pt",
"bytes": 18188823609,
"sha256": "50980756fad38bbefeb326ac0012e39f431ec5af8c0caaac8bd254f179f33746",
"top_level_keys": ["module"],
"tensor_count": 1382
},
"source": {
"repo": "JinchuanTian/bagpiper_sft",
"repo_type": "dataset",
"revision": "b11d5a0c11ad488edd04e3734d4bdff764977f57",
"path": "exp/opuslm_v2_stage2_pretrain_base/checkpoints/step_270000/global_step269985/mp_rank_00_model_states.pt",
"bytes": 18188293839,
"sha256": "e0d8fdd57c8cb8c938cd61ada43b20e469d9cea33ad8450e0971f80d4f9667b6"
},
"validation": {
"source_tensors_equal": 1381,
"added_tensor": "vocab_weight",
"strict_load": "pass",
"espnet_revision": "1992821bfb6d68c6636d1b75340b8b2b591bc015",
"transformers": "5.5.4",
"pytorch": "2.12.1+cu129",
"gpu": "NVIDIA H100 80GB HBM3"
}
}
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