Instructions to use espnet/geolid_vl107only_independent_trainable with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ESPnet
How to use espnet/geolid_vl107only_independent_trainable with ESPnet:
unknown model type (must be text-to-speech or automatic-speech-recognition)
- Notebooks
- Google Colab
- Kaggle
Download evaluation_previous.json from espnet/geolid_vl107only_independent_trainable: direct link, hf CLI and curl.
- Browser
- Download file 1.63 kB
-
https://huggingface.co/espnet/geolid_vl107only_independent_trainable/resolve/main/evaluation_previous.json
- Command line
-
hf download hf://espnet/geolid_vl107only_independent_trainable/evaluation_previous.json
-
curl -L -o evaluation_previous.json https://huggingface.co/espnet/geolid_vl107only_independent_trainable/resolve/main/evaluation_previous.json
1.63 kB
| { | |
| "dataset": "VoxLingua107", | |
| "split": "dev", | |
| "utterances": 1609, | |
| "languages": 33, | |
| "metric": "utterance-level accuracy (%)", | |
| "entrypoint": "espnet2.bin.lid_inference", | |
| "implementation_revision": "ca777d59eb49519e3827e40cc34997f2123d6682", | |
| "device": "cuda", | |
| "dtype": "float32", | |
| "batch_size": 1, | |
| "num_workers": 8, | |
| "use_preprocessor": false, | |
| "fix_duration": false, | |
| "input_order": "wav.scp sorted by utterance ID; standard ESPnet streaming iterator", | |
| "manifest_sha256": "6ad3c8fc6c74fab2720fe186525fc3bd20c4ab7b1be35d227c5c7f5456ba6c54", | |
| "environment": { | |
| "python": "3.12.3 (main, Jun 19 2026, 12:46:00) [GCC 13.3.0]", | |
| "torch": "2.11.0+cu128", | |
| "torch_cuda_build": "12.8", | |
| "torchaudio": "2.11.0+cu128", | |
| "transformers": "5.14.1", | |
| "espnet_s3prl": "2026.5.14", | |
| "soundfile": "0.14.0" | |
| }, | |
| "repo_id": "espnet/geolid_vl107only_independent_trainable", | |
| "model_sha256": "a4a77caebec41b539899c2760b1216c6c6d68e3d5093b1095ed07b1e7f8a0064", | |
| "correct": 1509, | |
| "accuracy_percent": 93.78495960223742, | |
| "paper_accuracy_percent": 93.7, | |
| "source_hf_revision_before_layout_migration": "71c1ec0f546c65eddfaf45584042b9e15b0278aa", | |
| "batch_dependence": { | |
| "s3prl_frame_alignment": "S3PRL reports ceil(samples/320) frames and repeats the final frame only at the batch boundary. Shorter items can instead expose padded-query frames from Wav2Vec2.", | |
| "ecapa_tdnn": "Temporal convolutions and unmasked SE global means depend on the padded sequence length before the masked final pooling.", | |
| "mvn_and_final_pooling_mask_padding": true, | |
| "reported_measurement_batch_size": 1 | |
| } | |
| } | |