Instructions to use Reza2kn/visualears-fastconformer-fa-full-ab-fp8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use Reza2kn/visualears-fastconformer-fa-full-ab-fp8 with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("Reza2kn/visualears-fastconformer-fa-full-ab-fp8") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
Upload fp8_vs_base_eval_summary.json
Browse files
validation/fp8_vs_base_eval_summary.json
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{
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"n_eval": 200,
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"base_wer": 0.1838006230529595,
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"base_cer": 0.06584178498985802,
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"base_wall_seconds": 6.194460868835449,
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"base_peak_vram_mib": 588.24658203125,
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"fp8_wer": 0.1847741433021807,
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"fp8_cer": 0.06693711967545639,
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"fp8_wall_seconds": 10.259917259216309,
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"fp8_peak_vram_mib": 662.06494140625,
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"wer_relative_to_base": 1.0052966101694916,
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"cer_relative_to_base": 1.0166358595194085,
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"wer_retention_vs_base": 0.9947033898305083,
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"cer_retention_vs_base": 0.9833641404805915,
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"exact_normalized_transcript_match": 0.54,
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"exact_matches": 108,
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"word_position_agreement_rough": 0.9313197383609081,
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"note": "Transcript-level comparison on same 200 FLEURS-fa clips; not CTC-logit argmax parity."
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}
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