Release int4 Oído model (8.3 MB)

.gitattributes

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README.md

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+---
+license: cc-by-sa-4.0
+language:
+- en
+pipeline_tag: automatic-speech-recognition
+base_model: nvidia/stt_en_conformer_ctc_small
+tags:
+- esp32
+- esp32-s3
+- microcontroller
+- tinyml
+- edge-ai
+- on-device
+- int4
+- quantization-aware-training
+- conformer
+- ctc
+datasets:
+- openslr/librispeech_asr
+- mozilla-foundation/common_voice_17_0
+- facebook/voxpopuli
+- MLCommons/peoples_speech
+model-index:
+- name: oido-ctc-small-int4
+  results:
+  - task:
+      type: automatic-speech-recognition
+      name: Speech Recognition
+    dataset:
+      name: LibriSpeech (clean)
+      type: openslr/librispeech_asr
+      config: clean
+      split: test
+    metrics:
+    - type: wer
+      value: 4.61
+      name: WER (on-chip int4 arithmetic, greedy)
+  - task:
+      type: automatic-speech-recognition
+      name: Speech Recognition
+    dataset:
+      name: LibriSpeech (other)
+      type: openslr/librispeech_asr
+      config: other
+      split: test
+    metrics:
+    - type: wer
+      value: 9.98
+      name: WER (on-chip int4 arithmetic, greedy)
+---
+
+# Oído int4: 8.3 MB speech recognition for the ESP32-S3
+
+*¡Oído!* is Spanish kitchen slang for *heard, got it*.
+
+This is the compact profile of [Oído](https://github.com/lokutor-ai/oido): open-vocabulary English speech recognition
+that runs entirely on an ESP32-S3, with no cloud and no NPU. At **8.3 MB** it leaves a 6 MB app partition free on a 16 MB
+flash module for your own application code (`esp32/firmware/partitions_nemo4.csv`).
+
+| LibriSpeech WER (%) | test-clean | test-other | Size |
+|---|---|---|---|
+| **This model** (int4, greedy, on-chip arithmetic) | **4.61** | **9.98** | 8.3 MB |
+| [Oído int8](https://huggingface.co/lokutor-ai/oido-ctc-small-int8) | 3.70 | 8.23 | 14.0 MB |
+| Espressif MultiNet7 on the same chip (ESP-SR benchmark) | 8.5 | 21.3 | 2.9 MB |
+
+The int4 weights also run about 10% faster than int8 (estimated RTF 0.68–0.82 from exact QEMU instruction counts; not
+yet measured on silicon).
+
+## Use
+
+```bash
+git clone https://github.com/lokutor-ai/oido && cd oido
+esp32/tools/flash.sh /dev/ttyUSB0 models/nemo4.tnm      # ESP32-S3-DevKitC-1 N16R8 + INMP441 microphone
+```
+
+## How it was made
+
+We took NVIDIA's [`stt_en_conformer_ctc_small`](https://huggingface.co/nvidia/stt_en_conformer_ctc_small) (CC-BY-4.0)
+and fine-tuned it with 4-bit quantization-aware training for 8,000 steps. Linear layers inside the Conformer blocks use
+int4 per-channel weights; the front end and output head use int8; activations and attention are int8. The training
+used public corpora:
+- Common Voice 17 and VoxPopuli (CC0);
+- LibriSpeech, MLS English, AMI and VCTK (CC-BY-4.0);
+- People's Speech, with transcripts regenerated by NVIDIA parakeet-tdt-0.6b-v2;
+- OpenSLR 70 and 83 (CC-BY-SA-4.0).
+
+## License
+
+This model is released under CC-BY-SA-4.0: it is derived from NVIDIA's CC-BY-4.0 model and trained on data that
+includes share-alike sources. The Oído engine and firmware are GPLv3, with commercial licenses available from
+[Lokutor](https://lokutor.com).

nemo4.tnm

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tokenizer.model

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