geolid_vl107only_independent_trainable / evaluation_previous.json
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Report complete VoxLingua107 dev rerun with unified ESPnetLIDModel
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{
"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
}
}