Update README.md

README.md

@@ -25,49 +25,67 @@ model-index:
          value: 29.95
 ---
 # Wav2Vec2-Large-XLSR-Latvian
+
 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
 on the [Latvian Common Voice dataset](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", "lv", split="test[:2%]")
+
 processor = Wav2Vec2Processor.from_pretrained("jimregan/wav2vec2-large-xlsr-latvian-cv")
 model = Wav2Vec2ForCTC.from_pretrained("jimregan/wav2vec2-large-xlsr-latvian-cv")
+
 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)
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 print("Prediction:", processor.batch_decode(predicted_ids))
 print("Reference:", test_dataset["sentence"][:2])
 ```
+
 ## Evaluation
+
 The model can be evaluated as follows on the Latvian 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", "lv", split="test")
 wer = load_metric("wer")
+
 processor = Wav2Vec2Processor.from_pretrained("jimregan/wav2vec2-large-xlsr-latvian-cv")
 model = Wav2Vec2ForCTC.from_pretrained("jimregan/wav2vec2-large-xlsr-latvian-cv") 
 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):
@@ -75,7 +93,9 @@ 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)
+
 # Preprocessing the datasets.
 # We need to read the audio files as arrays
 def evaluate(batch):
@@ -85,8 +105,9 @@ def evaluate(batch):
 	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**: 29.95 %
-```