---
license: apache-2.0
language:
- en
metrics:
- wer
base_model:
- facebook/wav2vec2-base-960h
tags:
- pytorch
- 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)