Automatic Speech Recognition
PEFT
Safetensors
Transformers
Nepali
English
whisper
nepali
english
code-switching
lora
Instructions to use DontHash/neptrans-v4-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DontHash/neptrans-v4-lora with PEFT:
Task type is invalid.
- Transformers
How to use DontHash/neptrans-v4-lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="DontHash/neptrans-v4-lora")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DontHash/neptrans-v4-lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,178 Bytes
bbd0e54 | 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 30 31 32 33 34 35 36 37 38 39 | {
"verification_date": "2026-10-20",
"operating_system": "Windows",
"python": "3.13.7",
"package_versions": {
"faster-whisper": "1.2.1",
"ctranslate2": "4.8.1",
"torch": "2.13.0",
"transformers": "4.57.6",
"peft": "0.21.0",
"huggingface-hub": "0.36.2",
"safetensors": "0.8.0"
},
"benchmark_repeated": false,
"scope": "Functional packaging checks; not an independent quality evaluation",
"checks": {
"adapter_tensor_sha256_matches_original": true,
"safetensors_tensor_count": 1024,
"trainable_adapter_parameters": 28835840,
"processor": {
"status": "passed",
"mel_bins": 128,
"sample_rate": 16000
},
"transformers_peft_inference": {
"status": "passed",
"device": "cpu",
"dtype": "float32",
"base_revision": "06f233fe06e710322aca913c1bc4249a0d71fce1",
"local_files_only": true,
"clip_duration_seconds": 3.0,
"output_nonempty": true,
"output_character_count": 38,
"elapsed_seconds": 78.37,
"scope": "One short internal development clip; functional smoke test, not a quality benchmark"
}
}
}
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