Update README.md

README.md

@@ -26,98 +26,4 @@ model-index:
 
 # Wav2Vec2-Large-XLSR-53-Swedish
 
-Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Swedish using the [Common Voice](https://huggingface.co/datasets/common_voice)
-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("marma/wav2vec2-large-xlsr-swedish")
-model = Wav2Vec2ForCTC.from_pretrained("marma/wav2vec2-large-xlsr-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])
-```
-
-
-## Evaluation
-
-The model can be evaluated as follows on the Swedish test data of Common Voice.
-
-
-```python
-import torch
-import torchaudio
-from datasets import load_dataset, load_metric
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
-import re
-
-test_dataset = load_dataset("common_voice", "sv-SE", split="test")
-wer = load_metric("wer")
-
-processor = Wav2Vec2Processor.from_pretrained("marma/wav2vec2-large-xlsr-swedish")
-model = Wav2Vec2ForCTC.from_pretrained("marma/wav2vec2-large-xlsr-swedish")
-model.to("cuda")
-
-chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]'
-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):
-	batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
-	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)
-
-# Preprocessing the datasets.
-# We need to read the aduio files as arrays
-def evaluate(batch):
-	inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
-
-	with torch.no_grad():
-		logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
-
-    pred_ids = torch.argmax(logits, dim=-1)
-	batch["pred_strings"] = processor.batch_decode(pred_ids)
-	return batch
-
-result = test_dataset.map(evaluate, batched=True, batch_size=8)
-
-print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
-```
-
-**Test Result**: 23.33 %
-
-
-## Training
-
-The [NST Swedish Dictation](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-17/) was used for training.
+This model has moved [here](https://huggingface.co/KBLab/wav2vec2-large-xlsr-53-swedish)