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---
language: en
datasets:
- LIUM/tedlium
tags:
- speech
license: apache-2.0
---
# Wav2Vec2-Large-Tedlium
The Wav2Vec2 large model fine-tuned on the TEDLIUM corpus.
The model is initialised with Facebook's [Wav2Vec2 large LV-60k](https://huggingface.co/facebook/wav2vec2-large-lv60) checkpoint pre-trained on 60,000h of audiobooks from the LibriVox project. It is fine-tuned on 452h of TED talks from the [TEDLIUM](https://huggingface.co/datasets/LIUM/tedlium) corpus (Release 3). When using the model, make sure that your speech input is sampled at 16Khz.
The model achieves a word error rate (WER) of 8.4% on the dev set and 8.2% on the test set. [Training logs](https://wandb.ai/sanchit-gandhi/tedlium/runs/10c85yc4?workspace=user-sanchit-gandhi) document the training and evaluation progress over 50k steps of fine-tuning.
See [this notebook](https://colab.research.google.com/drive/1FjTsqbYKphl9kL-eILgUc-bl4zVThL8F?usp=sharing) for more information on how this model was fine-tuned.
# Usage
To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("sanchit-gandhi/wav2vec2-large-tedlium")
model = Wav2Vec2ForCTC.from_pretrained("sanchit-gandhi/wav2vec2-large-tedlium")
# load dummy dataset
ds = load_dataset("sanchit-gandhi/tedlium_dummy", split="validation")
# process audio inputs
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values # Batch size 1
# retrieve logits
logits = model(input_values).logits
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
print("Target: ", ds["text"][0])
print("Transcription: ", transcription[0])
```
## Evaluation
This code snippet shows how to evaluate **Wav2Vec2-Large-Tedlium** on the TEDLIUM test data.
```python
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import torch
from jiwer import wer
tedlium_eval = load_dataset("LIUM/tedlium", "release3", split="test")
model = Wav2Vec2ForCTC.from_pretrained("sanchit-gandhi/wav2vec2-large-tedlium").to("cuda")
processor = Wav2Vec2Processor.from_pretrained("sanchit-gandhi/wav2vec2-large-tedlium")
def map_to_pred(batch):
input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values
with torch.no_grad():
logits = model(input_values.to("cuda")).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
batch["transcription"] = transcription
return batch
result = tedlium_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["speech"])
print("WER:", wer(result["text"], result["transcription"]))
``` |