Update README.md

README.md

@@ -1,32 +1,57 @@
-language: {sv-SE} 
+---
+
+language: sv-SE
+
 datasets:
-- common_voice 
-metrics:
-- wer
+
+- common_voice
+
 tags:
+
 - audio
+
 - automatic-speech-recognition
+
 - speech
+
 - xlsr-fine-tuning-week
+
 license: apache-2.0
+
 model-index:
-- name: {MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-Swedish} 
+
+- name: wav2vec2-large-xlsr-53-Swedish by Mehdi Hosseini Moghadam
+
   results:
+
   - task: 
+
       name: Speech Recognition
+
       type: automatic-speech-recognition
+
     dataset:
-      name: Common Voice {sv-SE}
+
+      name: Common Voice sv-SE
+
       type: common_voice
-      args: {sv-SE}
+
+      args: sv-SE
+
     metrics:
+
        - name: Test WER
+
          type: wer
-         value: {41.388337}
+
+         value:  41.388337
+
 ---
 
+# 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)
 
-Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on {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
@@ -34,91 +59,123 @@ 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", "{sv-SE}", split="test[:2%]") 
+test_dataset = load_dataset("common_voice", "sv-SE", split="test[:2%]").
+
+processor = Wav2Vec2Processor.from_pretrained("MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-Swedish")
 
-processor = Wav2Vec2Processor.from_pretrained("{MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-Swedish}") 
-model = Wav2Vec2ForCTC.from_pretrained("{MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-Swedish}") 
+model = Wav2Vec2ForCTC.from_pretrained("MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-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. 
-
+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") 
+test_dataset = load_dataset("common_voice", "sv-SE", split="test")
+
 wer = load_metric("wer")
 
-processor = Wav2Vec2Processor.from_pretrained("{MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-Swedish}")
-model = Wav2Vec2ForCTC.from_pretrained("{MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-Swedish}") 
+processor = Wav2Vec2Processor.from_pretrained("MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-Swedish")
+
+model = Wav2Vec2ForCTC.from_pretrained("MehdiHosseiniMoghadam/wav2vec2-large-xlsr-53-Swedish")
+
 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
 
 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)
+    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**: 41.388337 %  
+```
 
+**Test Result**:  41.388337 %
 
 ## Training
 
-The Common Voice `train`, `validation`
-
+The Common Voice `train`, `validation` datasets were used for training.