Update README.md

README.md

@@ -37,7 +37,7 @@ model-index:
          value: 19.145252414798616
 ---
 # Wav2vec 2.0 base-voxpopuli-sv-swedish
-Finetuned version of Facebooks [VoxPopuli-sv large](https://huggingface.co/facebook/wav2vec2-large-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%**.
+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.
 
@@ -49,8 +49,8 @@ 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")
+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