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README.md

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+---
+language: ca
+datasets:
+- common_voice 
+- parlament_parla
+metrics:
+- wer
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- speech-to-text
+license: apache-2.0
+model-index:
+- name: Catalan VoxPopuli Wav2Vec2 Large
+  results:
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    datasets:
+      - name: Common Voice ca
+        type: common_voice
+        args: ca
+      - name: ParlamentParla
+        url: https://www.openslr.org/59/
+    metrics:
+       - name: Test WER
+         type: wer
+         value: TBD
+       - name: Google Crowsourced Corpus WER
+         type: wer
+         value: TBD
+       - name: Audiobook “La llegenda de Sant Jordi” WER
+         type: wer
+         value: TBD
+---
+
+# Wav2Vec2-Large-100k-VoxPopuli-Català
+
+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.
+
+**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.
+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.
+
+You can find training and evaluation scripts in the github repository [ccoreilly/wav2vec2-catala](https://github.com/ccoreilly/wav2vec2-catala)
+
+When using this model, make sure that your speech input is sampled at 16kHz.
+
+## Results
+
+Word error rate was evaluated on the following datasets unseen by the model:
+
+| Dataset | WER |
+| ------- | --- |
+| [Test split CV+ParlamentParla]((https://github.com/ccoreilly/wav2vec2-catala/blob/master/test.csv)) | TBD |
+| [Google Crowsourced Corpus](https://www.openslr.org/69/) | TBD |
+| Audiobook “La llegenda de Sant Jordi” | TBD | 
+
+
+## Usage
+
+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", "ca", split="test[:2%]")
+
+processor = Wav2Vec2Processor.from_pretrained("ccoreilly/wav2vec2-large-100k-voxpopuli-catala") 
+model = Wav2Vec2ForCTC.from_pretrained("ccoreilly/wav2vec2-large-100k-voxpopuli-catala")
+
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+
+# Preprocessing the datasets.
+# We need to read the audio 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])
+```

config.json

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+{
+  "_name_or_path": "facebook/wav2vec2-large-100k-voxpopuli",
+  "activation_dropout": 0.0,
+  "apply_spec_augment": true,
+  "architectures": [
+    "Wav2Vec2ForCTC"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 1,
+  "conv_bias": true,
+  "conv_dim": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
+    10,
+    3,
+    3,
+    3,
+    3,
+    2,
+    2
+  ],
+  "conv_stride": [
+    5,
+    2,
+    2,
+    2,
+    2,
+    2,
+    2
+  ],
+  "ctc_loss_reduction": "mean",
+  "ctc_zero_infinity": false,
+  "do_stable_layer_norm": true,
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
+  "feat_extract_dropout": 0.0,
+  "feat_extract_norm": "layer",
+  "feat_proj_dropout": 0.0,
+  "final_dropout": 0.0,
+  "gradient_checkpointing": true,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.1,
+  "mask_channel_length": 10,
+  "mask_channel_min_space": 1,
+  "mask_channel_other": 0.0,
+  "mask_channel_prob": 0.0,
+  "mask_channel_selection": "static",
+  "mask_feature_length": 10,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_space": 1,
+  "mask_time_other": 0.0,
+  "mask_time_prob": 0.05,
+  "mask_time_selection": "static",
+  "model_type": "wav2vec2",
+  "num_attention_heads": 16,
+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 24,
+  "pad_token_id": 41,
+  "transformers_version": "4.4.0.dev0",
+  "vocab_size": 42
+}

preprocessor_config.json

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+{
+  "do_normalize": true,
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "return_attention_mask": true,
+  "sampling_rate": 16000
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4dac4b85a42301201abc8ed8de711d87eb4bd33d8897e501b61fd8c031ec1559
+size 1262106007

special_tokens_map.json

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+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "[UNK]", "pad_token": "[PAD]"}

tokenizer_config.json

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+{"unk_token": "[UNK]", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "[PAD]", "do_lower_case": false, "word_delimiter_token": "|"}

vocab.json

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+{"-": 0, "h": 1, "c": 2, "b": 3, "é": 4, "e": 5, "x": 6, "r": 7, "'": 8, "o": 9, "j": 10, "a": 11, "è": 12, "ó": 13, "ç": 15, "ï": 16, "s": 17, "f": 18, "ú": 19, "g": 20, "q": 21, "·": 22, "u": 23, "n": 24, "y": 25, "í": 26, "à": 27, "k": 28, "ü": 29, "w": 30, "v": 31, "ò": 32, "p": 33, "t": 34, "m": 35, "z": 36, "d": 37, "i": 38, "l": 39, "|": 14, "[UNK]": 40, "[PAD]": 41}