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Model: khanhld/wav2vec2-base-vietnamese-160h
Source: Original Platform

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README.md

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+---
+language: vi
+datasets:
+- vivos
+- common_voice
+- FOSD
+- VLSP
+metrics:
+- wer
+pipeline_tag: automatic-speech-recognition
+tags:
+- audio
+- speech
+- Transformer
+- wav2vec2
+- automatic-speech-recognition
+- vietnamese
+license: cc-by-nc-4.0
+widget:
+- example_title: common_voice_vi_30519758.mp3
+  src: https://huggingface.co/khanhld/wav2vec2-base-vietnamese-160h/raw/main/examples/common_voice_vi_30519758.mp3
+- example_title: VIVOSDEV15_020.wav
+  src: https://huggingface.co/khanhld/wav2vec2-base-vietnamese-160h/raw/main/examples/VIVOSDEV15_020.wav
+model-index:
+- name: Wav2vec2 Base Vietnamese 160h
+  results:
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: common-voice-vietnamese
+      type: common_voice
+      args: vi
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 10.78
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: VIVOS
+      type: vivos
+      args: vi
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 15.05
+---
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/wav2vec2-base-vietnamese-160h/speech-recognition-on-common-voice-vi)](https://paperswithcode.com/sota/speech-recognition-on-common-voice-vi?p=wav2vec2-base-vietnamese-160h)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/wav2vec2-base-vietnamese-160h/speech-recognition-on-vivos)](https://paperswithcode.com/sota/speech-recognition-on-vivos?p=wav2vec2-base-vietnamese-160h)
+
+# Vietnamese Speech Recognition using Wav2vec 2.0
+### Table of contents
+1. [Model Description](#description)
+2. [Implementation](#implementation)
+3. [Benchmark Result](#benchmark)
+4. [Example Usage](#example)
+5. [Evaluation](#evaluation)
+6. [Citation](#citation)
+7. [Contact](#contact)
+
+<a name = "description" ></a>
+### Model Description
+Fine-tuned the Wav2vec2-based model on about 160 hours of Vietnamese speech dataset from different resources, including [VIOS](https://huggingface.co/datasets/vivos), [COMMON VOICE](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0), [FOSD](https://data.mendeley.com/datasets/k9sxg2twv4/4) and [VLSP 100h](https://drive.google.com/file/d/1vUSxdORDxk-ePUt-bUVDahpoXiqKchMx/view). We have not yet incorporated the Language Model into our ASR system but still gained a promising result.
+<a name = "implementation" ></a>
+### Implementation
+We also provide code for Pre-training and Fine-tuning the Wav2vec2 model. If you wish to train on your dataset, check it out here:
+- [Pre-train code](https://github.com/khanld/Wav2vec2-Pretraining)
+- [Fine-tune code](https://github.com/khanld/ASR-Wa2vec-Finetune)
+
+<a name = "benchmark" ></a>
+### Benchmark WER Result
+| | [VIVOS](https://huggingface.co/datasets/vivos) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) | 
+|---|---|---|
+|without LM| 15.05 | 10.78 |
+|with LM| in progress | in progress |
+
+<a name = "example" ></a>
+### Example Usage [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1blz1KclnIfbOp8o2fW3WJgObOQ9SMGBo?usp=sharing)
+```python
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+import librosa
+import torch
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+processor = Wav2Vec2Processor.from_pretrained("khanhld/wav2vec2-base-vietnamese-160h")
+model = Wav2Vec2ForCTC.from_pretrained("khanhld/wav2vec2-base-vietnamese-160h")
+model.to(device)
+
+def transcribe(wav):
+  input_values = processor(wav, sampling_rate=16000, return_tensors="pt").input_values
+  logits = model(input_values.to(device)).logits
+  pred_ids = torch.argmax(logits, dim=-1)
+  pred_transcript = processor.batch_decode(pred_ids)[0]
+  return pred_transcript
+
+
+wav, _ = librosa.load('path/to/your/audio/file', sr = 16000)
+print(f"transcript: {transcribe(wav)}")
+
+```
+
+<a name = "evaluation"></a>
+### Evaluation [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1XQCq4YGLnl23tcKmYeSwaksro4IgC_Yi?usp=sharing)
+
+```python
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+from datasets import load_dataset
+import torch
+import re
+from datasets import load_dataset, load_metric, Audio
+
+wer = load_metric("wer")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# load processor and model
+processor = Wav2Vec2Processor.from_pretrained("khanhld/wav2vec2-base-vietnamese-160h")
+model = Wav2Vec2ForCTC.from_pretrained("khanhld/wav2vec2-base-vietnamese-160h")
+model.to(device)
+model.eval()
+
+# Load dataset
+test_dataset = load_dataset("mozilla-foundation/common_voice_8_0", "vi", split="test", use_auth_token="your_huggingface_auth_token")
+test_dataset = test_dataset.cast_column("audio", Audio(sampling_rate=16000))
+chars_to_ignore = r'[,?.!\-;:"“%\'<EFBFBD>]' # ignore special characters
+
+# preprocess data
+def preprocess(batch):
+  audio = batch["audio"]
+  batch["input_values"] = audio["array"]
+  batch["transcript"] = re.sub(chars_to_ignore, '', batch["sentence"]).lower()
+  return batch
+
+# run inference
+def inference(batch):
+  input_values = processor(batch["input_values"], 
+                            sampling_rate=16000, 
+                            return_tensors="pt").input_values
+  logits = model(input_values.to(device)).logits
+  pred_ids = torch.argmax(logits, dim=-1)
+  batch["pred_transcript"] = processor.batch_decode(pred_ids) 
+  return batch
+  
+test_dataset = test_dataset.map(preprocess)
+result = test_dataset.map(inference, batched=True, batch_size=1)
+print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_transcript"], references=result["transcript"])))
+```
+**Test Result**: 10.78%
+
+<a name = "citation" ></a>
+### Citation 
+[![DOI](https://zenodo.org/badge/491468343.svg)](https://zenodo.org/badge/latestdoi/491468343)
+<strong>BibTeX</strong>
+```
+@mics{Duy_Khanh_Finetune_Wav2vec_2_0_2022,
+  author = {Duy Khanh, Le},
+  doi = {10.5281/zenodo.6542357},
+  license = {CC-BY-NC-4.0},
+  month = {5},
+  title = {{Finetune Wav2vec 2.0 For Vietnamese Speech Recognition}},
+  url = {https://github.com/khanld/ASR-Wa2vec-Finetune},
+  year = {2022}
+}
+```
+<strong>APA</strong>
+```
+Duy Khanh, L. (2022). Finetune Wav2vec 2.0 For Vietnamese Speech Recognition [Data set]. https://doi.org/10.5281/zenodo.6542357
+```
+
+<a name = "contact"></a>
+### Contact
+- khanhld218@gmail.com
+- [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/)
+- [![LinkedIn](https://img.shields.io/badge/linkedin-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/khanhld257/)
+
+

config.json

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+{
+  "_name_or_path": "facebook/wav2vec2-base",
+  "activation_dropout": 0.0,
+  "adapter_kernel_size": 3,
+  "adapter_stride": 2,
+  "add_adapter": false,
+  "apply_spec_augment": true,
+  "architectures": [
+    "Wav2Vec2ForCTC"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 1,
+  "classifier_proj_size": 256,
+  "codevector_dim": 256,
+  "contrastive_logits_temperature": 0.1,
+  "conv_bias": false,
+  "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,
+  "diversity_loss_weight": 0.1,
+  "do_stable_layer_norm": false,
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
+  "feat_extract_norm": "group",
+  "feat_proj_dropout": 0.1,
+  "feat_quantizer_dropout": 0.0,
+  "final_dropout": 0.0,
+  "freeze_feat_extract_train": true,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.0,
+  "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_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_min_space": 1,
+  "mask_time_other": 0.0,
+  "mask_time_prob": 0.05,
+  "mask_time_selection": "static",
+  "model_type": "wav2vec2",
+  "no_mask_channel_overlap": false,
+  "no_mask_time_overlap": false,
+  "num_adapter_layers": 3,
+  "num_attention_heads": 12,
+  "num_codevector_groups": 2,
+  "num_codevectors_per_group": 320,
+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 12,
+  "num_negatives": 100,
+  "output_hidden_size": 768,
+  "pad_token_id": 95,
+  "proj_codevector_dim": 256,
+  "tdnn_dilation": [
+    1,
+    2,
+    3,
+    1,
+    1
+  ],
+  "tdnn_dim": [
+    512,
+    512,
+    512,
+    512,
+    1500
+  ],
+  "tdnn_kernel": [
+    5,
+    3,
+    3,
+    1,
+    1
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.18.0",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 96,
+  "xvector_output_dim": 512
+}

preprocessor_config.json

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+{
+  "do_normalize": true,
+  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "Wav2Vec2Processor",
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}

pytorch_model.bin

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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": "|", "replace_word_delimiter_char": " ", "tokenizer_class": "Wav2Vec2CTCTokenizer", "processor_class": "Wav2Vec2Processor"}

vocab.json

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