KhayaAI
/

w2v-bert-xon

@@ -1,199 +1,157 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
 ## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+language:
+  - xon
+license: apache-2.0
 library_name: transformers
+pipeline_tag: automatic-speech-recognition
+tags:
+  - speech
+  - asr
+  - african-languages
+  - w2v-bert
+  - khaya
+  - xon
+metrics:
+  - wer
+model-index:
+  - name: w2v-bert-xon
+    results:
+      - task:
+          type: automatic-speech-recognition
+          name: Automatic Speech Recognition
+        metrics:
+          - type: wer
+            value: 15.7
+            name: WER
 ---
+# w2v-bert-xon — Konkomba (Likpakpaanl) ASR
+**DONDO** (Decolonial Oral Neural Dialect Ontology) open speech-recognition base model for **Konkomba (Likpakpaanl)** (Ghana).
+Fine-tuned from the **w2v-BERT 2.0** self-supervised speech encoder.
+- **Language:** Konkomba (Likpakpaanl) (`xon`)
+- **Task:** Automatic Speech Recognition (CTC)
+- **Backbone:** w2v-BERT 2.0
+- **License:** Apache-2.0 (attribution only; commercial use permitted)
+- **Part of:** the DONDO family — <https://huggingface.co/KhayaAI>
+## Intended use
+A **base model** for Konkomba (Likpakpaanl) ASR. Use it directly to transcribe read or relatively
+clean speech, or as an initialisation to **fine-tune on your own in-domain data**
+(conversational, broadcast, clinical, etc.) with comparatively little labelled audio.
+DONDO is also intended as an **open test bed**: a shared, reproducible model on
+which new low-resource speech techniques can be demonstrated for the benefit of
+all. Aligned research groups are welcome to build on it and collaborate.
+## Training data
+Primarily read speech derived from **religious texts** paired with verified
+transcripts in the standard orthography. Such data is license-clear, exists for
+many otherwise under-resourced languages, and is orthographically consistent. Its
+main limitation is domain narrowness, which is why this is released as a base model.
 ## Evaluation
+| Metric | Value |
+|---|---|
+| WER (in-domain test) | **15.7%** |
+WER is computed on in-domain test material and should not be read as a guarantee
+for other domains.
+## How to use
+```python
+import torch, torchaudio
+from transformers import AutoProcessor, AutoModelForCTC
+model_id = "KhayaAI/w2v-bert-xon"
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForCTC.from_pretrained(model_id)
+speech, sr = torchaudio.load("audio.wav")
+if sr != 16000:
+    speech = torchaudio.functional.resample(speech, sr, 16000)
+inputs = processor(speech.squeeze().numpy(), sampling_rate=16000, return_tensors="pt")
+with torch.no_grad():
+    logits = model(**inputs).logits
+pred_ids = torch.argmax(logits, dim=-1)
+print(processor.batch_decode(pred_ids)[0])
+```
+## Limitations
+Trained largely on read religious speech, so the model may underperform on
+spontaneous, code-switched or noisy audio until fine-tuned. Orthographic
+conventions vary across communities; evaluation for the smallest languages rests
+on limited test sets.
+## License
+Released under the **Apache-2.0** license. You are free to use, modify, redistribute and build upon this model, **including for commercial purposes**. The only substantive requirement is **attribution**.
+## Professional services & hosted APIs
+This model is free to use under Apache-2.0. If your team would like help
+**deploying it offline on your own infrastructure and data**, Khaya AI offers
+professional services (integration, fine-tuning and on-premises deployment). For
+ready-to-use and more advanced ASR, including **APIs** for interested parties,
+see Khaya Studio.
+- Khaya AI — <https://khaya.ai>
+- Khaya Studio — <https://studio.khaya.ai>
+- Khaya Studio ASR — <https://studio.khaya.ai/asr>
+## Citation
+```bibtex
+@techreport{dondo2026,
+  title  = {DONDO: Open w2v-BERT Speech Recognition Base Models for African Languages},
+  author = {{Khaya AI}},
+  year   = {2026},
+  institution = {Khaya AI},
+  note   = {Decolonial Oral Neural Dialect Ontology. Funded by the Huniki Federation. https://huggingface.co/KhayaAI}
+}
+```
+## Acknowledgements
+Funded by the **Huniki Federation**. We thank **Ghana-NLP** and **Algorine Research** for their support with benchmarking, testing and data, and **Hugging Face** for compute credits. We also thank the language communities and data contributors whose recordings and transcriptions made this work possible.
+---