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README.md ADDED
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+ ---
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+ language: ca
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+ datasets:
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+ - common_voice
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+ - parlament_parla
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+ metrics:
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+ - wer
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+ tags:
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+ - audio
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+ - automatic-speech-recognition
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+ - speech
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+ - speech-to-text
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+ license: apache-2.0
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+ model-index:
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+ - name: Catalan VoxPopuli Wav2Vec2 Large
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+ results:
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+ - task:
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+ name: Speech Recognition
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+ type: automatic-speech-recognition
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+ datasets:
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+ - name: Common Voice ca
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+ type: common_voice
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+ args: ca
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+ - name: ParlamentParla
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+ url: https://www.openslr.org/59/
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+ metrics:
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+ - name: Test WER
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+ type: wer
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+ value: TBD
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+ - name: Google Crowsourced Corpus WER
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+ type: wer
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+ value: TBD
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+ - name: Audiobook “La llegenda de Sant Jordi” WER
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+ type: wer
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+ value: TBD
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+ ---
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+
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+ # Wav2Vec2-Large-100k-VoxPopuli-Català
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+
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+ 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.
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+
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+ **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.
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+ 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.
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+
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+ You can find training and evaluation scripts in the github repository [ccoreilly/wav2vec2-catala](https://github.com/ccoreilly/wav2vec2-catala)
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+
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+ When using this model, make sure that your speech input is sampled at 16kHz.
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+
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+ ## Results
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+
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+ Word error rate was evaluated on the following datasets unseen by the model:
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+
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+ | Dataset | WER |
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+ | ------- | --- |
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+ | [Test split CV+ParlamentParla]((https://github.com/ccoreilly/wav2vec2-catala/blob/master/test.csv)) | TBD |
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+ | [Google Crowsourced Corpus](https://www.openslr.org/69/) | TBD |
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+ | Audiobook “La llegenda de Sant Jordi” | TBD |
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+
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+
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+ ## Usage
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+
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+ The model can be used directly (without a language model) as follows:
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+
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+ ```python
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+ import torch
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+ import torchaudio
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+ from datasets import load_dataset
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+ from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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+
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+ test_dataset = load_dataset("common_voice", "ca", split="test[:2%]")
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+
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+ processor = Wav2Vec2Processor.from_pretrained("ccoreilly/wav2vec2-large-100k-voxpopuli-catala")
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+ model = Wav2Vec2ForCTC.from_pretrained("ccoreilly/wav2vec2-large-100k-voxpopuli-catala")
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+
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+ resampler = torchaudio.transforms.Resample(48_000, 16_000)
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+
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+ # Preprocessing the datasets.
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+ # We need to read the audio files as arrays
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+ def speech_file_to_array_fn(batch):
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+ speech_array, sampling_rate = torchaudio.load(batch["path"])
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+ batch["speech"] = resampler(speech_array).squeeze().numpy()
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+ return batch
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+
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+ test_dataset = test_dataset.map(speech_file_to_array_fn)
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+ inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
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+
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+ with torch.no_grad():
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+ logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
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+
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+ predicted_ids = torch.argmax(logits, dim=-1)
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+
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+ print("Prediction:", processor.batch_decode(predicted_ids))
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+ print("Reference:", test_dataset["sentence"][:2])
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+ ```
config.json ADDED
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+ "apply_spec_augment": true,
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+ "architectures": [
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+ "Wav2Vec2ForCTC"
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+ ],
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+ "attention_dropout": 0.1,
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+ "bos_token_id": 1,
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+ "conv_bias": true,
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+ "conv_dim": [
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+ 512,
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+ "ctc_loss_reduction": "mean",
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+ "ctc_zero_infinity": false,
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+ "do_stable_layer_norm": true,
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+ "eos_token_id": 2,
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+ "feat_extract_activation": "gelu",
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+ "feat_extract_dropout": 0.0,
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+ "feat_extract_norm": "layer",
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+ "feat_proj_dropout": 0.0,
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+ "final_dropout": 0.0,
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+ "gradient_checkpointing": true,
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+ "hidden_act": "gelu",
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+ "hidden_dropout": 0.1,
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+ "hidden_size": 1024,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 4096,
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+ "layer_norm_eps": 1e-05,
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+ "layerdrop": 0.1,
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+ "mask_channel_length": 10,
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+ "mask_channel_min_space": 1,
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+ "mask_channel_other": 0.0,
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+ "mask_channel_prob": 0.0,
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+ "mask_channel_selection": "static",
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+ "mask_feature_length": 10,
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+ "mask_feature_prob": 0.0,
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+ "mask_time_length": 10,
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+ "mask_time_min_space": 1,
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+ "mask_time_other": 0.0,
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+ "mask_time_prob": 0.05,
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+ "mask_time_selection": "static",
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+ "model_type": "wav2vec2",
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+ "num_attention_heads": 16,
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+ "num_conv_pos_embedding_groups": 16,
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+ "num_conv_pos_embeddings": 128,
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+ "num_feat_extract_layers": 7,
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+ "num_hidden_layers": 24,
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+ "pad_token_id": 41,
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+ "transformers_version": "4.4.0.dev0",
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+ "vocab_size": 42
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+ }
preprocessor_config.json ADDED
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+ "return_attention_mask": true,
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+ "sampling_rate": 16000
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+ }
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