Update README.md

README.md

@@ -26,7 +26,7 @@ model-index:
          value: 42.14 
 ---
 
-Wav2Vec2-Large-XLSR-53-Bemba
+# Wav2Vec2-Large-XLSR-53-Bemba
 
 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Bemba using the [BembaSpeech](https://csikasote.github.io/BembaSpeech). When using this model, make sure that your speech input is sampled at 16kHz.
 
@@ -50,15 +50,15 @@ 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)
 
@@ -86,30 +86,30 @@ processor = Wav2Vec2Processor.from_pretrained("csikasote/wav2vec2-large-xlsr-bem
 model = Wav2Vec2ForCTC.from_pretrained("csikasote/wav2vec2-large-xlsr-bemba") 
 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):
-\\\\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
-\\\\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
-\\\\tbatch["speech"] = speech_array.squeeze().numpy() 
-\\\\treturn batch
+\\\\\\\\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
+\\\\\\\\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
+\\\\\\\\tbatch["speech"] = 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):
-\\\\tinputs = 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)
 
-\\\\twith torch.no_grad():
-\\\\t\\\\tlogits = 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
 
-\\\\tpred_ids = torch.argmax(logits, dim=-1)
-\\\\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
-\\\\treturn batch
+\\\\\\\\tpred_ids = torch.argmax(logits, dim=-1)
+\\\\\\\\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
+\\\\\\\\treturn batch
 
 result = test_dataset.map(evaluate, batched=True, batch_size=8)