README.md

---
library_name: transformers
base_model:
- eddiegulay/wav2vec2-large-xlsr-mvc-swahili
tags:
- automatic-speech-recognition
- wav2vec2
- swahili
- huggingface
- transformers
- common-voice
license: apache-2.0
datasets:
- mozilla-foundation/common_voice_11_0
language:
- sw
metrics:
- bleu
- wer
- rouge
- f1
---

# 🇰🇪 Model Card for `RareElf/swahili-wav2vec2-asr`

This model is a fine-tuned version of [`eddiegulay/wav2vec2-large-xlsr-mvc-swahili`](https://huggingface.co/eddiegulay/wav2vec2-large-xlsr-mvc-swahili) for **automatic speech recognition (ASR)** in **Swahili**. It has been trained using the [Common Voice 11.0 Swahili dataset](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0).

---

## 📋 Model Details

### Model Description

This model leverages the `wav2vec2` architecture from Facebook AI, fine-tuned for Swahili ASR. It maps raw speech waveforms sampled at 16kHz to transcriptions using a CTC (Connectionist Temporal Classification) loss. It supports real-time transcription for voice-based Swahili applications.

- **Developed by:** Kevin Obote / RareElf
- **Funded by:** Internal research at Guild Code
- **Shared by:** RareElf
- **Model type:** Automatic Speech Recognition (ASR)
- **Language(s) (NLP):** Swahili (`sw`)
- **License:** Apache-2.0
- **Finetuned from model:** `eddiegulay/wav2vec2-large-xlsr-mvc-swahili`

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** https://huggingface.co/RareElf/swahili-wav2vec2-asr
- **Paper [optional]:** Coming soon
- **Demo [optional]:** Coming soon on semasasa.ai

## 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. -->

This model can be used for:

- Transcribing Swahili audio for accessibility, journalism, documentation, education, etc.
- Integration into chatbots or voice agents in Swahili.

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

- Can be integrated with translation and sentiment analysis pipelines.
- Useful for fine-tuning on domain-specific Swahili data (e.g.education, healthcare, government).

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

- Not suitable for noisy, far-field, or multi-speaker environments without preprocessing.
- Not recommended for use in legal, medical, or high-stakes domains without additional validation.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->


- The model may underperform on underrepresented dialects of Swahili.
- Accents, noisy recordings, and overlapping speech may impact accuracy.
- Reflects the linguistic distribution of Common Voice contributors, which may not be representative of all Swahili speakers.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

- Preprocess noisy audio for best results.
- Fine-tune further on targeted domain data for production use.
- Provide user disclaimers about ASR limitations in live deployments.

## How to Get Started with the Model

Use the code below to get started with the model.

```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import torch
import librosa

model_id = "RareElf/swahili-wav2vec2-asr"

processor = Wav2Vec2Processor.from_pretrained(model_id)
model = Wav2Vec2ForCTC.from_pretrained(model_id)

audio, _ = librosa.load("sample.wav", sr=16000)
inputs = processor(audio, return_tensors="pt", sampling_rate=16000).input_values

with torch.no_grad():
    logits = model(inputs).logits

predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)[0]
print(transcription)
```

## 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. -->

- **Dataset:** Common Voice 11.0 – Swahili subset


### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
- Fine-tuned using `Trainer` API from 🤗 Transformers.
- **Loss:** CTC (Connectionist Temporal Classification)
- **Optimizer:** AdamW
- **Precision:** `fp16` (mixed precision)
  
#### Preprocessing [optional]

- Resampled to 16kHz
- Normalized text
- Removed empty, corrupted, or misaligned samples


#### Training Hyperparameters

- **Training regime:**
- **Epochs:** 10  
- **Batch Size:** 16  
- **Learning Rate:** 3e-4  
- **Warmup Steps:** 500  
- **Weight Decay:** 0.01  
- **Gradient Accumulation:** 2  <!--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. -->

- **Dataset:** Held-out subset of Common Voice Swahili

#### 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. -->

- WER (Word Error Rate)
- BLEU (for translation use-case)
- ROUGE (for paraphrase quality)

### Results

| Metric | Score |
|--------|-------|
| WER    | 0.33  |
| BLEU   | 0.44 |
| ROUGE  | 0.66  |
> *Note: Evaluation scores are being finalized with the full test set.*

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

- Visualized attention maps confirm the model learns phonetic and acoustic patterns relevant to Swahili.

## 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:** ***  
- **Hours used:** ~10  
- **Cloud Provider:** Google Cloud  
- **Compute Region:** ****  
- **Carbon Emitted:** ~X gCO2eq (estimation via [mlco2](https://mlco2.github.io/impact))


## Technical Specifications [optional]

### Model Architecture and Objective

- **Architecture:** Wav2Vec2 (base) + CTC head  
- **Objective:** Predict character-level transcription from 16kHz audio

### Compute Infrastructure

[More Information Needed]

#### Hardware

** Personal Computer Lenovo ThinkPad T14 Gen 1 (32GB RAM) 1TB SSD

#### Software

- Python 3.10  
- PyTorch 2.x  
- Transformers 4.39.x  
- Datasets 2.x  

## 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. -->

- **ASR:** Automatic Speech Recognition

- **WER:** Word Error Rate

- **CTC:** Connectionist Temporal Classification

## More Information [optional]

- [Common Voice Dataset Card](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0)
- [Transformers Documentation](https://huggingface.co/docs/transformers)

## Model Card Authors [optional]

- **Kevin Obote/ RareElf / Guild Code Team**

## Model Card Contact

- **Email:** [[email protected]](mailto:[email protected])  
- **GitHub:** [Kevin Obote](https://github.com/Kevinobote)