Update README.md

README.md

@@ -9,4 +9,67 @@ base_model:
 - openai/whisper-small
 pipeline_tag: automatic-speech-recognition
 library_name: transformers
----
+tags:
+- audio
+- automatic-speech-recognition
+---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+
+## Model Details
+
+### Model Description
+
+This model is a fine-tuned version of OpenAI's Whisper-small, optimized for isiZulu Automatic Speech Recognition (ASR). It has been trained on the NCHLT isiZUlu Speech Corpus to improve its performance on isiXhosa speech transcription tasks.
+
+### Base Model
+Name: openai/whisper-small
+Type: Automatic Speech Recognition (ASR)
+Original language: Multilingual
+
+### Performance
+- Word Error Rate (WER): 31.87%
+- Character Error Rate (CER): 9.43%
+
+### Usage
+To use this model for inference:
+```
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+import torch
+
+# Load model and processor
+model = WhisperForConditionalGeneration.from_pretrained("nmoyo45/zu_whisper")
+processor = WhisperProcessor.from_pretrained("nmoyo45/zu_whisper")
+
+# 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)
+```
+
+### Dataset:
+#### NCHLT isiZulu Speech Corpus:
+- Size: Approximately 56 hours of transcribed speech
+- Speakers: 210 (98 female, 112 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)
+- Citation: N.J. de Vries, M.H. Davel, J. Badenhorst, W.D. Basson, F. de Wet, E. Barnard and A. de Waal, "A smartphone-based ASR data collection tool for under-resourced languages", Speech Communication, Volume 56, January 2014, pp 119–131.	
+
+#### Lwazi isiZulu ASR Corpus:
+- Speakers: 199 Speakers
+- Content: ~14 elicited utterances, ~16 phonetically balanced read sentences
+- License: Creative Commons Attribution 2.5 South Africa License: http://creativecommons.org/licenses/by/2.5/za/legalcode
+- Citation: E. Barnard, M. Davel and C. van Heerden, "ASR Corpus Design for Resource-Scarce Languages," in Proceedings of the 10th Annual Conference of the International Speech Communication Association (Interspeech), Brighton, United Kingdom, September 2009, pp. 2847-2850.