# Wav2vec 2.0 base-voxpopuli-sv-swedish
Finetuned version of Facebooks [VoxPopuli-sv base](https://huggingface.co/facebook/wav2vec2-base-sv-voxpopuli) model using 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 **5.62%**, WER for Common Voice test set is **19.15%**.

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

## 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-base-voxpopuli-sv-swedish")
model = Wav2Vec2ForCTC.from_pretrained("KBLab/wav2vec2-base-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"]}
transformers pytorch wav2vec2 automatic-speech-recognition audio speech voxpopuli license:cc-by-nc-4.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #voxpopuli #license-cc-by-nc-4.0 #endpoints_compatible #region-us 

# Wav2vec 2.0 base-voxpopuli-sv-swedish
Finetuned version of Facebooks VoxPopuli-sv base model using 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 5.62%, WER for Common Voice test set is 19.15%.

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

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

# Wav2vec 2.0 base-voxpopuli-sv-swedish
Finetuned version of Facebooks VoxPopuli-sv base model using 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 5.62%, WER for Common Voice test set is 19.15%.

When using this model, make sure that your speech input is sampled at 16kHz. ## Usage
The model can be used directly (without a language model) as follows:
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #voxpopuli #license-cc-by-nc-4.0 #endpoints_compatible #region-us 
 # Wav2vec 2.0 base-voxpopuli-sv-swedish
Finetuned version of Facebooks VoxPopuli-sv base model using 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 5.62%, WER for Common Voice test set is 19.15%.

When using this model, make sure that your speech input is sampled at 16kHz. ## Usage
The model can be used directly (without a language model) as follows:
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #voxpopuli #license-cc-by-nc-4.0 #endpoints_compatible #region-us 
# Wav2vec 2.0 base-voxpopuli-sv-swedish
Finetuned version of Facebooks VoxPopuli-sv base model using 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 5.62%, WER for Common Voice test set is 19.15%.

When using this model, make sure that your speech input is sampled at 16kHz.## Usage
The model can be used directly (without a language model) as follows: