1 ---
2 language: id
3 datasets:
4 - common_voice
5 metrics:
6 - wer
7 tags:
8 - audio
9 - automatic-speech-recognition
10 - speech
11 - xlsr-fine-tuning-week
12 license: apache-2.0
13 model-index:
14 - name: XLSR Wav2Vec2 Indonesian by Indonesian NLP
15 results:
16 - task:
17 name: Speech Recognition
18 type: automatic-speech-recognition
19 dataset:
20 name: Common Voice id
21 type: common_voice
22 args: id
23 metrics:
24 - name: Test WER
25 type: wer
26 value: 14.29
27 ---
28
29 # Wav2Vec2-Large-XLSR-Indonesian
30
31 This is the model for Wav2Vec2-Large-XLSR-Indonesian, a fine-tuned
32 [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
33 model on the [Indonesian Common Voice dataset](https://huggingface.co/datasets/common_voice).
34 When using this model, make sure that your speech input is sampled at 16kHz.
35
36 ## Usage
37 The model can be used directly (without a language model) as follows:
38 ```python
39 import torch
40 import torchaudio
41 from datasets import load_dataset
42 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
43
44 test_dataset = load_dataset("common_voice", "id", split="test[:2%]")
45
46 processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-large-xlsr-indonesian")
47 model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-large-xlsr-indonesian")
48
49
50 # Preprocessing the datasets.
51 # We need to read the aduio files as arrays
52 def speech_file_to_array_fn(batch):
53 speech_array, sampling_rate = torchaudio.load(batch["path"])
54 resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
55 batch["speech"] = resampler(speech_array).squeeze().numpy()
56 return batch
57
58 test_dataset = test_dataset.map(speech_file_to_array_fn)
59 inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
60
61 with torch.no_grad():
62 logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
63
64 predicted_ids = torch.argmax(logits, dim=-1)
65
66 print("Prediction:", processor.batch_decode(predicted_ids))
67 print("Reference:", test_dataset[:2]["sentence"])
68 ```
69
70
71 ## Evaluation
72
73 The model can be evaluated as follows on the Indonesian test data of Common Voice.
74
75 ```python
76 import torch
77 import torchaudio
78 from datasets import load_dataset, load_metric
79 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
80 import re
81
82 test_dataset = load_dataset("common_voice", "id", split="test")
83 wer = load_metric("wer")
84
85 processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-large-xlsr-indonesian")
86 model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-large-xlsr-indonesian")
87 model.to("cuda")
88
89 chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\'\”\�]'
90
91
92 # Preprocessing the datasets.
93 # We need to read the aduio files as arrays
94 def speech_file_to_array_fn(batch):
95 batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
96 speech_array, sampling_rate = torchaudio.load(batch["path"])
97 resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
98 batch["speech"] = resampler(speech_array).squeeze().numpy()
99 return batch
100
101 test_dataset = test_dataset.map(speech_file_to_array_fn)
102
103 # Preprocessing the datasets.
104 # We need to read the aduio files as arrays
105 def evaluate(batch):
106 inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
107
108 with torch.no_grad():
109 logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
110
111 pred_ids = torch.argmax(logits, dim=-1)
112 batch["pred_strings"] = processor.batch_decode(pred_ids)
113 return batch
114
115 result = test_dataset.map(evaluate, batched=True, batch_size=8)
116
117 print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
118 ```
119
120 **Test Result**: 14.29 %
121
122 ## Training
123
124 The Common Voice `train`, `validation`, and [synthetic voice datasets](https://cloud.uncool.ai/index.php/s/Kg4C6f5NJGN9ZdR) were used for training.
125
126 The script used for training can be found [here](https://github.com/indonesian-nlp/wav2vec2-indonesian)
127