Update README.md

README.md

@@ -33,92 +33,125 @@ When using this model, make sure that your speech input is sampled at 16kHz.
 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", "es, split="test[:2%]").
+test_dataset = load_dataset("common_voice", "es", split="test[:2%]").
 
 processor = Wav2Vec2Processor.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-spanish")
+
 model = Wav2Vec2ForCTC.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-spanish")
 
 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
+
+  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
+
+  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 Spanish test data of Common Voice.
-
+The model can be evaluated as follows on the Ukrainian 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", "es", split="test")
+
 wer = load_metric("wer")
 
 processor = Wav2Vec2Processor.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-spanish")
+
 model = Wav2Vec2ForCTC.from_pretrained("mrm8488/wav2vec2-large-xlsr-53-spanish")
+
 model.to("cuda")
 
-chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]'
+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
+
+  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
+  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
+  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**: ??? %
+```
 
+**Test Result**:  %
 
 ## Training
 
 The Common Voice `train`, `validation` datasets were used for training.
 
-The script used for training can be found ???
+The script used for training can be found ???