Update README.md (#2)

- Update README.md (78304c7672625788e976a8b3ed76064dac3ea508)


Co-authored-by: Lucas Tramonte <[email protected]>

README.md

@@ -11,4 +11,58 @@ tags:
 - Transformers
 - speech
 - audio
----
+---
+
+# Model Description
+
+This model is a fine-tuned version of facebook/wav2vec2-base-960h for automatic speech recognition (ASR). 
+It has been trained using the [LibriSpeech dataset](https://paperswithcode.com/dataset/librispeech) and is designed to improve transcription accuracy over the base model.
+
+The fine-tuning process involved:
+
+- Selecting a subset of speakers from the `dev-clean` and `test-clean` datasets.
+- Preprocessing audio files and their corresponding transcriptions.
+- Training with gradient accumulation, mixed precision (if available), and periodic evaluation.
+- Saving the fine-tuned model for inference.
+
+*[GitHub](https://github.com/LucasTramonte/SpeechRecognition)* 
+*Authors*: Lucas Tramonte, Kiyoshi Araki
+
+# Usage
+
+To transcribe audio files, the model can be used as follows:
+
+```python
+from transformers import AutoProcessor, AutoModelForCTC
+import torch
+import librosa
+
+# Load model and processor
+processor = AutoProcessor.from_pretrained("deepl-project/conformer-finetunning")
+model = AutoModelForCTC.from_pretrained("deepl-project/conformer-finetunning")
+
+# Load and preprocess an audio file
+file_path = "path/to/audio/file.wav"
+speech, sr = librosa.load(file_path, sr=16000)
+inputs = processor(speech, sampling_rate=sr, return_tensors="pt", padding=True)
+
+# Perform inference
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+# Decode transcription
+predicted_ids = torch.argmax(logits, dim=-1)
+transcription = processor.batch_decode(predicted_ids)
+
+print("Transcription:", transcription[0])
+```
+
+
+# References
+
+- [LibriSpeech Dataset](https://paperswithcode.com/dataset/librispeech)
+- [Conformer Model Paper](https://paperswithcode.com/paper/conformer-based-target-speaker-automatic)
+- [Whisper Model Paper](https://arxiv.org/abs/2212.04356)
+
+
+