1 ---
2 language: ca
3 datasets:
4 - common_voice
5 - parlament_parla
6 metrics:
7 - wer
8 tags:
9 - audio
10 - automatic-speech-recognition
11 - speech
12 - speech-to-text
13 license: apache-2.0
14 model-index:
15 - name: Catalan VoxPopuli Wav2Vec2 Large
16 results:
17 - task:
18 name: Speech Recognition
19 type: automatic-speech-recognition
20 datasets:
21 - name: Common Voice ca
22 type: common_voice
23 args: ca
24 - name: ParlamentParla
25 url: https://www.openslr.org/59/
26 metrics:
27 - name: Test WER
28 type: wer
29 value: 5.98
30 - name: Google Crowsourced Corpus WER
31 type: wer
32 value: 12.14
33 - name: Audiobook “La llegenda de Sant Jordi” WER
34 type: wer
35 value: 12.02
36 ---
37
38 # Wav2Vec2-Large-100k-VoxPopuli-Català
39
40 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) on Catalan language using the [Common Voice](https://huggingface.co/datasets/common_voice) and [ParlamentParla](https://www.openslr.org/59/) datasets.
41
42 **Attention:** The split train/dev/test used does not fully map with the CommonVoice 6.1 dataset. A custom split was used combining both the CommonVoice and ParlamentParla dataset and can be found [here](https://github.com/ccoreilly/wav2vec2-catala). Evaluating on the CV test dataset will produce a biased WER as 1144 audio files of that dataset were used in training/evaluation of this model.
43 WER was calculated using this [test.csv](https://github.com/ccoreilly/wav2vec2-catala/blob/master/test-filtered.csv) which was not seen by the model during training/evaluation.
44
45 You can find training and evaluation scripts in the github repository [ccoreilly/wav2vec2-catala](https://github.com/ccoreilly/wav2vec2-catala)
46
47 When using this model, make sure that your speech input is sampled at 16kHz.
48
49 ## Results
50
51 Word error rate was evaluated on the following datasets unseen by the model:
52
53 | Dataset | WER |
54 | ------- | --- |
55 | [Test split CV+ParlamentParla]((https://github.com/ccoreilly/wav2vec2-catala/blob/master/test-filtered.csv)) | 5.98% |
56 | [Google Crowsourced Corpus](https://www.openslr.org/69/) | 12.14% |
57 | Audiobook “La llegenda de Sant Jordi” | 12.02% |
58
59
60 ## Usage
61
62 The model can be used directly (without a language model) as follows:
63
64 ```python
65 import torch
66 import torchaudio
67 from datasets import load_dataset
68 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
69
70 test_dataset = load_dataset("common_voice", "ca", split="test[:2%]")
71
72 processor = Wav2Vec2Processor.from_pretrained("ccoreilly/wav2vec2-large-100k-voxpopuli-catala")
73 model = Wav2Vec2ForCTC.from_pretrained("ccoreilly/wav2vec2-large-100k-voxpopuli-catala")
74
75 resampler = torchaudio.transforms.Resample(48_000, 16_000)
76
77 # Preprocessing the datasets.
78 # We need to read the audio files as arrays
79 def speech_file_to_array_fn(batch):
80 speech_array, sampling_rate = torchaudio.load(batch["path"])
81 batch["speech"] = resampler(speech_array).squeeze().numpy()
82 return batch
83
84 test_dataset = test_dataset.map(speech_file_to_array_fn)
85 inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
86
87 with torch.no_grad():
88 logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
89
90 predicted_ids = torch.argmax(logits, dim=-1)
91
92 print("Prediction:", processor.batch_decode(predicted_ids))
93 print("Reference:", test_dataset["sentence"][:2])
94 ```