# Wav2vec 2.0 large-voxpopuli-sv-swedish

**PLEASE NOTE that [this](https://huggingface.co/KBLab/wav2vec2-large-voxrex-swedish) model performs better and has a less restrictive license.**


Additionally pretrained and finetuned version of Facebooks [VoxPopuli-sv large](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) model using Swedish radio broadcasts, NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is **3.95%**. WER for Common Voice test set is **10.99%** directly and **7.82%** with a 4-gram language model.

When using this model, make sure that your speech input is sampled at 16kHz.

## Training
This model has additionally pretrained on 1000h of Swedish local radio broadcasts, fine-tuned for 120000 updates on NST + CommonVoice and then for an additional 20000 updates on CommonVoice only. The additional fine-tuning on CommonVoice hurts performance on the NST+CommonVoice test set somewhat and, unsurprisingly, improves it on the CommonVoice test set. It seems to perform generally better though [citation needed].

## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "sv-SE", split="test[:2%]").
processor = Wav2Vec2Processor.from_pretrained("KBLab/wav2vec2-large-voxpopuli-sv-swedish")
model = Wav2Vec2ForCTC.from_pretrained("KBLab/wav2vec2-large-voxpopuli-sv-swedish")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
{"language": "sv-SE", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition", "speech", "voxpopuli"], "datasets": ["common_voice", "NST Swedish ASR Database"], "metrics": ["wer", "cer"], "model-index": [{"name": "Wav2vec 2.0 large VoxPopuli-sv swedish", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice", "type": "common_voice", "args": "sv-SE"}, "metrics": [{"type": "wer", "value": 10.994764, "name": "Test WER"}, {"type": "cer", "value": 3.946846, "name": "Test CER"}]}]}]}
transformers pytorch jax wav2vec2 automatic-speech-recognition audio speech voxpopuli license:cc-by-nc-4.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #voxpopuli #license-cc-by-nc-4.0 #model-index #endpoints_compatible #region-us 

# Wav2vec 2.0 large-voxpopuli-sv-swedish

PLEASE NOTE that this model performs better and has a less restrictive license.


Additionally pretrained and finetuned version of Facebooks VoxPopuli-sv large model using Swedish radio broadcasts, NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is 3.95%. WER for Common Voice test set is 10.99% directly and 7.82% with a 4-gram language model.

When using this model, make sure that your speech input is sampled at 16kHz.

## Training
This model has additionally pretrained on 1000h of Swedish local radio broadcasts, fine-tuned for 120000 updates on NST + CommonVoice and then for an additional 20000 updates on CommonVoice only. The additional fine-tuning on CommonVoice hurts performance on the NST+CommonVoice test set somewhat and, unsurprisingly, improves it on the CommonVoice test set. It seems to perform generally better though [citation needed].

## Usage
The model can be used directly (without a language model) as follows:

# Wav2vec 2.0 large-voxpopuli-sv-swedish

PLEASE NOTE that this model performs better and has a less restrictive license.


Additionally pretrained and finetuned version of Facebooks VoxPopuli-sv large model using Swedish radio broadcasts, NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is 3.95%. WER for Common Voice test set is 10.99% directly and 7.82% with a 4-gram language model.

When using this model, make sure that your speech input is sampled at 16kHz. ## Training
This model has additionally pretrained on 1000h of Swedish local radio broadcasts, fine-tuned for 120000 updates on NST + CommonVoice and then for an additional 20000 updates on CommonVoice only. The additional fine-tuning on CommonVoice hurts performance on the NST+CommonVoice test set somewhat and, unsurprisingly, improves it on the CommonVoice test set. It seems to perform generally better though [citation needed]. ## Usage
The model can be used directly (without a language model) as follows:
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #voxpopuli #license-cc-by-nc-4.0 #model-index #endpoints_compatible #region-us 
 # Wav2vec 2.0 large-voxpopuli-sv-swedish

PLEASE NOTE that this model performs better and has a less restrictive license.


Additionally pretrained and finetuned version of Facebooks VoxPopuli-sv large model using Swedish radio broadcasts, NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is 3.95%. WER for Common Voice test set is 10.99% directly and 7.82% with a 4-gram language model.

When using this model, make sure that your speech input is sampled at 16kHz. ## Training
This model has additionally pretrained on 1000h of Swedish local radio broadcasts, fine-tuned for 120000 updates on NST + CommonVoice and then for an additional 20000 updates on CommonVoice only. The additional fine-tuning on CommonVoice hurts performance on the NST+CommonVoice test set somewhat and, unsurprisingly, improves it on the CommonVoice test set. It seems to perform generally better though [citation needed]. ## Usage
The model can be used directly (without a language model) as follows:
passage: TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #voxpopuli #license-cc-by-nc-4.0 #model-index #endpoints_compatible #region-us 
# Wav2vec 2.0 large-voxpopuli-sv-swedish

PLEASE NOTE that this model performs better and has a less restrictive license.


Additionally pretrained and finetuned version of Facebooks VoxPopuli-sv large model using Swedish radio broadcasts, NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is 3.95%. WER for Common Voice test set is 10.99% directly and 7.82% with a 4-gram language model.

When using this model, make sure that your speech input is sampled at 16kHz.## Training
This model has additionally pretrained on 1000h of Swedish local radio broadcasts, fine-tuned for 120000 updates on NST + CommonVoice and then for an additional 20000 updates on CommonVoice only. The additional fine-tuning on CommonVoice hurts performance on the NST+CommonVoice test set somewhat and, unsurprisingly, improves it on the CommonVoice test set. It seems to perform generally better though [citation needed].## Usage
The model can be used directly (without a language model) as follows: