Update README.md

README.md

@@ -44,19 +44,19 @@ import torchaudio
 from datasets import load_dataset
 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 
-!wget https://www.openslr.org/resources/43/ne_np_female.zip
-!unzip ne_np_female.zip
-!ls ne_np_female
+!wget https://www.openslr.org/resources/43/km_kh_male.zip
+!unzip km_kh_male.zip
+!ls km_kh_male
 
 colnames=['path','sentence'] 
-df  = pd.read_csv('/content/ne_np_female/line_index.tsv',sep='\\\\\\\\t',header=None,names = colnames)
-df['path'] = '/content/ne_np_female/wavs/'+df['path'] +'.wav'
+df  = pd.read_csv('/content/km_kh_male/line_index.tsv',sep='\\\\\\\\\\\\\\\\t',header=None,names = colnames)
+df['path'] = '/content/km_kh_male/wavs/'+df['path'] +'.wav'
 
 train, test = train_test_split(df, test_size=0.1)
 
-test.to_csv('/content/ne_np_female/line_index_test.csv')
+test.to_csv('/content/km_kh_male/line_index_test.csv')
 
-test_dataset = load_dataset('csv', data_files='/content/ne_np_female/line_index_test.csv',split = 'train')
+test_dataset = load_dataset('csv', data_files='/content/km_kh_male/line_index_test.csv',split = 'train')
 
 processor = Wav2Vec2Processor.from_pretrained("gagan3012/wav2vec2-xlsr-nepali")
 model = Wav2Vec2ForCTC.from_pretrained("gagan3012/wav2vec2-xlsr-nepali") 
@@ -66,15 +66,15 @@ 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):
-\\\\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
-\\\\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
-\\\\treturn batch
+\\\\\\\\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
+\\\\\\\\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
+\\\\\\\\treturn 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():
-\\\\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
+\\\\\\\\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
 
 predicted_ids = torch.argmax(logits, dim=-1)
 
@@ -120,37 +120,38 @@ processor = Wav2Vec2Processor.from_pretrained("gagan3012/wav2vec2-xlsr-khmer")
 model = Wav2Vec2ForCTC.from_pretrained("gagan3012/wav2vec2-xlsr-khmer") 
 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["text"] = re.sub(chars_to_ignore_regex, '', batch["text"]).lower()
-	speech_array, sampling_rate = torchaudio.load(batch["path"])
-	batch["speech"] = resampler(speech_array).squeeze().numpy()
-	return batch
+\tbatch["text"] = re.sub(chars_to_ignore_regex, '', batch["text"]).lower()
+\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
+\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
+\treturn 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)
+\tinputs = 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
+\twith torch.no_grad():
+\t\tlogits = 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
+\tpred_ids = torch.argmax(logits, dim=-1)
+\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
+\treturn batch
 
 cer = load_metric("cer")
 
 result = test_dataset.map(evaluate, batched=True, batch_size=8)
 
 print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["text"])))
-print("CER: {:2f}".format(100 * cer.compute(predictions=result["pred_strings"], references=result["text"])))```
+print("CER: {:2f}".format(100 * cer.compute(predictions=result["pred_strings"], references=result["text"])))
+```
 
 **Test Result**: 24.96 %