From 6088709f54b55463c4a62ba8519fe20ee90a8b1b Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Mon, 18 May 2026 18:22:52 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: KBLab/wav2vec2-large-xlsr-53-swedish Source: Original Platform --- .gitattributes | 17 +++++ README.md | 136 +++++++++++++++++++++++++++++++++++++++ config.json | 68 ++++++++++++++++++++ flax_model.msgpack | 3 + preprocessor_config.json | 8 +++ pytorch_model.bin | 3 + special_tokens_map.json | 2 + tokenizer_config.json | 1 + vocab.json | 1 + 9 files changed, 239 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 config.json create mode 100644 flax_model.msgpack create mode 100644 preprocessor_config.json create mode 100644 pytorch_model.bin create mode 100644 special_tokens_map.json create mode 100644 tokenizer_config.json create mode 100644 vocab.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..d699711 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,17 @@ +*.bin.* filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tar.gz filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..9125882 --- /dev/null +++ b/README.md @@ -0,0 +1,136 @@ +--- +language: sv +datasets: +- common_voice +- KTH/nst +metrics: +- wer +- cer +tags: +- audio +- automatic-speech-recognition +- speech +- xlsr-fine-tuning-week +license: apache-2.0 +model-index: +- name: XLSR Wav2Vec2 Swedish by KBLab + results: + - task: + name: Speech Recognition + type: automatic-speech-recognition + dataset: + name: Common Voice sv-SE + type: common_voice + args: sv-SE + metrics: + - name: Test WER + type: wer + value: 14.298610 + - name: Test CER + type: cer + value: 4.925294 +--- + +# Wav2Vec2-Large-XLSR-53-Swedish + +Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Swedish using the [NST Swedish Dictation](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-17/). +When using this model, make sure that your speech input is sampled at 16kHz. + +**Note:** We recommend using our newer model [wav2vec2-large-voxrex-swedish](https://huggingface.co/KBLab/wav2vec2-large-voxrex-swedish) for the best performance. + +## 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", "sv-SE", split="test[:2%]"). + +processor = Wav2Vec2Processor.from_pretrained("KBLab/wav2vec2-large-xlsr-53-swedish") +model = Wav2Vec2ForCTC.from_pretrained("KBLab/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. + + +```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") +wer = load_metric("wer") + +processor = Wav2Vec2Processor.from_pretrained("KBLab/wav2vec2-large-xlsr-53-swedish") +model = Wav2Vec2ForCTC.from_pretrained("KBLab/wav2vec2-large-xlsr-53-swedish") +model.to("cuda") + +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) + 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"]))) +print("CER: {:2f}".format(100 * wer.compute(predictions=[" ".join(list(entry)) for entry in result["pred_strings"]], references=[" ".join(list(entry)) for entry in result["sentence"]]))) +``` + +**WER**: 14.298610% +**CER**: 4.925294% + +## Training + +First the XLSR model was further pre-trained for 50 epochs with a corpus consisting of 1000 hours spoken Swedish from various radio stations. Secondly [NST Swedish Dictation](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-17/) was used for fine tuning as well as [Common Voice](https://commonvoice.mozilla.org/en/datasets). Lastly only Common Voice dataset was used for final finetuning. The [Fairseq](https://github.com/fairseq) scripts were used. diff --git a/config.json b/config.json new file mode 100644 index 0000000..fc56b90 --- /dev/null +++ b/config.json @@ -0,0 +1,68 @@ +{ + "activation_dropout": 0.1, + "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": "sum", + "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": 0.1, + "final_dropout": 0.1, + "gradient_checkpointing": false, + "hidden_act": "gelu", + "hidden_dropout": 0.1, + "hidden_dropout_prob": 0.1, + "hidden_size": 1024, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-05, + "layerdrop": 0.1, + "mask_feature_length": 10, + "mask_feature_prob": 0.0, + "mask_time_length": 10, + "mask_time_prob": 0.05, + "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": 0, + "transformers_version": "4.4.0.dev0", + "vocab_size": 46 +} diff --git a/flax_model.msgpack b/flax_model.msgpack new file mode 100644 index 0000000..3b85790 --- /dev/null +++ b/flax_model.msgpack @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ccb34bc1a20832a02087d80e50174c2b0c8aba25bc4771fc7b7285616a59d4ed +size 1261958872 diff --git a/preprocessor_config.json b/preprocessor_config.json new file mode 100644 index 0000000..0886a48 --- /dev/null +++ b/preprocessor_config.json @@ -0,0 +1,8 @@ +{ + "do_normalize": true, + "feature_size": 1, + "padding_side": "right", + "padding_value": 0.0, + "return_attention_mask": true, + "sampling_rate": 16000 +} diff --git a/pytorch_model.bin b/pytorch_model.bin new file mode 100644 index 0000000..f1a6837 --- /dev/null +++ b/pytorch_model.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:64da056c24b86761464f7ba8e51325a7578778846e24c0486959a0c7f717ee4b +size 1262116567 diff --git a/special_tokens_map.json b/special_tokens_map.json new file mode 100644 index 0000000..f172940 --- /dev/null +++ b/special_tokens_map.json @@ -0,0 +1,2 @@ +{"bos_token": "", "eos_token": "", "unk_token": "", "pad_token": ""} + diff --git a/tokenizer_config.json b/tokenizer_config.json new file mode 100644 index 0000000..ba5abdc --- /dev/null +++ b/tokenizer_config.json @@ -0,0 +1 @@ +{"unk_token": "", "bos_token": "", "eos_token": "", "pad_token": "", "do_lower_case": true, "return_attention_mask": false, "do_normalize": true} diff --git a/vocab.json b/vocab.json new file mode 100644 index 0000000..b595a63 --- /dev/null +++ b/vocab.json @@ -0,0 +1 @@ +{"": 0, "": 1, "": 2, "": 3, "|": 4, "T": 5, "E": 6, "A": 7, "N": 8, "R": 9, "S": 10, "I": 11, "L": 12, "D": 13, "O": 14, "M": 15, "K": 16, "G": 17, "U": 18, "V": 19, "F": 20, "H": 21, "\u00c4": 22, "\u00c5": 23, "P": 24, "\u00d6": 25, "B": 26, "J": 27, "C": 28, "Y": 29, "X": 30, "W": 31, "Z": 32, "\u00c9": 33, "Q": 34, "8": 35, "7": 36, "6": 37, "5": 38, "3": 39, "2": 40, "4": 41, "9": 42, "1": 43, "0": 44, "'": 45}