File size: 4,395 Bytes
ab8a3de 1cb4863 ab8a3de 1cb4863 ab8a3de 1cb4863 ab8a3de 1cb4863 ab8a3de 1cb4863 ab8a3de 1cb4863 ab8a3de 1cb4863 ab8a3de | 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 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 | ---
license: apache-2.0
base_model: facebook/omniASR-CTC-1B
datasets:
- google/WaxalNLP
language:
- ln
- sn
library_name: fairseq2
pipeline_tag: automatic-speech-recognition
tags:
- automatic-speech-recognition
- omnilingual-asr
- lingala
- shona
- waxal
- zindi
model-index:
- name: waxal-joint-ctc-1b-lid
results:
- task:
type: automatic-speech-recognition
dataset:
name: WaxalNLP held-out validation split (pooled Lingala + Shona)
type: google/WaxalNLP
metrics:
- name: CER
type: cer
value: 0.0885
- name: WER
type: wer
value: 0.328
---
# waxal-joint-ctc-1b-lid
Joint Lingala + Shona OmniASR CTC-1B, fine-tuned **without any language tag**, paired with
the text language-identification classifier used to route its output.
This is the entry point of the [WAXAL ASR solution](https://github.com/DariusTheGeek/waxal-asr-solution).
It exists so the pipeline can determine each clip's language **from the audio**: a tag-free
decode of every clip, then a character n-gram classification of the decoded text.
## What is in this repository
| File | Bytes | Role |
|---|---:|---|
| `model.pt` | 3,903,024,817 | OmniASR CTC-1B v2, joint LIN+SNA, step 3033 |
| `omniASR_tokenizer_written_v2.model` | 91,481 | shared char tokenizer (identical across all OmniASR v2 families) |
| `text_lid_train_only.joblib` | 1,910,530 | TF-IDF char 3–5 gram + logistic regression language classifier |
| `card.yaml` | — | fairseq2 asset card |
| `requirements.txt` | — | pinned inference runtime |
## How routing works
1. `model.pt` decodes every clip once, with **no language conditioning** — the model has one
recognizer, one shared encoder, one shared CTC head, and no language tag, prompt, ID, or
embedding input.
2. `text_lid_train_only.joblib` classifies the resulting transcript as Lingala or Shona.
3. Each clip is routed to its language-specific decoder stack.
The classifier is fitted **only on gold training transcripts** (32,328 rows), never on model
output and never on test data. At inference it is applied to hypothesis strings.
On the 892-clip evaluation set it assigns 447 clips to Lingala and 445 to Shona.
## Model contract
```
recognizers: 1 shared_encoder: true shared_tokenizer: true
shared_ctc_head: true language_tag: false language_prompt: false
language_id: false language_embedding: false
metadata_input: false model_level_routing: false
```
## Training
| | |
|---|---|
| Parent | `facebook/omniASR-CTC-1B` (v2, untouched official init) |
| Seed | 42 |
| Parallelism | DDP, world size 4 |
| Batch | 2 per GPU × 4 accumulation × 4 GPUs = 32 |
| Updates | 4,044 total; released checkpoint at **step 3033** |
| Peak LR | 1.0e-5, tri-stage 0.1/0.4/0.5 |
| Precision | bfloat16, activation checkpointing every layer |
Character and word error on the held-out validation split:
| Split | CER | WER |
|---|---|---|
| pooled | 0.0885 | 0.3280 |
| Lingala | 0.1345 | 0.3579 |
| Shona | 0.0492 | 0.2894 |
Standalone it is weaker than the language-specific models it routes to;
its value is that one object serves both languages, so a single decode of the full test set
yields the routing decision.
## Usage
```python
from huggingface_hub import snapshot_download
import joblib, sklearn
d = snapshot_download("DariusTheGeek/waxal-joint-ctc-1b-lid")
assert sklearn.__version__ == "1.5.2" # the classifier was fitted under this version
lid = joblib.load(f"{d}/text_lid_train_only.joblib")
lid.predict(["mbote na yo", "mhoro sei"]) # -> ['lin', 'sna']
```
For the ASR half, point fairseq2 at `card.yaml` after substituting
`@WAXAL_MODEL_DIR@`, or use the solution repository's CLI, which wraps the whole
contract (pinned environment, batch-size-1 decode, tokenizer wiring):
```bash
git clone https://github.com/DariusTheGeek/waxal-asr-solution
cd waxal-asr-solution && bash install.sh
python models/download_models.py --repo waxal-joint-ctc-1b-lid
.venvs/omni/bin/python inference/decode/omniasr.py \
--config configs/joint/joint.yaml \
--audio path/to/wav_dir --output hypotheses.csv
.venvs/fuse/bin/python inference/route/route.py \
--hypotheses hypotheses.csv \
--model artifacts/text_lid_train_only.joblib --output route.csv
```
`requirements.txt` in this repo pins the inference runtime.
## Licence
`apache-2.0`, inherited from the OmniASR parent.
|