Update README.md

README.md

@@ -16,4 +16,74 @@ metrics:
 - wer
 base_model: openai/whisper-small
 license: apache-2.0
----
+---
+
+---
+language:
+- xh
+pipeline_tag: automatic-speech-recognition
+tags:
+- audio
+- automatic-speech-recognition
+widget:
+- example_title: Librispeech sample 1
+  src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
+- example_title: Librispeech sample 2
+  src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
+datasets:
+- Beijuka/xhosa_parakeet_50hr
+metrics:
+- wer
+base_model: openai/whisper-small
+---
+
+# Whisper-Small Fine-tuned for isiXhosa ASR
+
+## Model Description
+This model is a fine-tuned version of OpenAI's Whisper-small, optimized for isiXhosa Automatic Speech Recognition (ASR). It has been trained on the NCHLT isiXhosa Speech Corpus to improve its performance on isiXhosa speech transcription tasks.
+
+## Performance
+- Word Error Rate (WER): 32%
+
+## Base Model
+- Name: openai/whisper-small
+- Type: Automatic Speech Recognition (ASR)
+- Original language: Multilingual
+
+## Usage
+To use this model for inference:
+
+```python
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+import torch
+
+# Load model and processor
+model = WhisperForConditionalGeneration.from_pretrained("TheirStory-Inc/whisper-small-xhosa")
+processor = WhisperProcessor.from_pretrained("TheirStory-Inc/whisper-small-xhosa")
+
+# Prepare your audio file (16kHz sampling rate)
+audio_input = ...  # Load your audio file here
+
+# Process the audio
+input_features = processor(audio_input, sampling_rate=16000, return_tensors="pt").input_features
+
+# Generate token ids
+predicted_ids = model.generate(input_features)
+
+# Decode the token ids to text
+transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
+
+print(transcription)
+```
+
+## Fine-tuning Dataset
+- Name: NCHLT isiXhosa Speech Corpus
+- Size: Approximately 56 hours of transcribed speech
+- Speakers: 209 (106 female, 103 male)
+- Content: Prompted speech (3-5 word utterances read from a smartphone screen)
+- Source: Audio recordings smartphone-collected in non-studio environment
+- License: Creative Commons Attribution 3.0 Unported License (CC BY 3.0)
+- 
+```bibtext
+De Vries, N.J., Davel, M.H., Badenhorst, J., Basson, W.D., de Wet, F., Barnard, E. and de Waal, A. (2014). A smartphone-based ASR data collection tool for under-resourced languages. Speech Communication, 56, 119-131. https://hdl.handle.net/20.500.12185/279
+```