Initial my model

3gram_long.bin.zip

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README.md

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+---
+language: vi
+datasets:
+- vivos
+- common_voice
+metrics:
+- wer
+pipeline_tag: automatic-speech-recognition
+tags:
+- audio
+- speech
+- speechbrain
+- Transformer
+license: cc-by-nc-4.0
+widget:
+- example_title: Example 1
+  src: https://huggingface.co/dragonSwing/wav2vec2-base-vn-270h/raw/main/example.mp3
+- example_title: Example 2
+  src: https://huggingface.co/dragonSwing/wav2vec2-base-vn-270h/raw/main/example2.mp3
+model-index:
+- name: Wav2vec2 Base Vietnamese 270h
+  results:
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice vi
+      type: common_voice
+      args: vi
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 9.66
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice 7.0
+      type: mozilla-foundation/common_voice_7_0
+      args: vi
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 5.57
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice 8.0
+      type: mozilla-foundation/common_voice_8_0
+      args: vi
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 5.76
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: VIVOS
+      type: vivos
+      args: vi
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 3.70
+---
+# Wav2Vec2-Base-Vietnamese-270h
+Fine-tuned Wav2Vec2 model on Vietnamese Speech Recognition task using about 270h labelled data combined from multiple datasets including [Common Voice](https://huggingface.co/datasets/common_voice), [VIVOS](https://huggingface.co/datasets/vivos), [VLSP2020](https://vlsp.org.vn/vlsp2020/eval/asr). The model was fine-tuned using SpeechBrain toolkit with a custom tokenizer. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io/).  
+When using this model, make sure that your speech input is sampled at 16kHz.  
+Please refer to [huggingface blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) or [speechbrain](https://github.com/speechbrain/speechbrain/tree/develop/recipes/CommonVoice/ASR/CTC) on how to fine-tune Wav2Vec2 model on a specific language.
+
+### Benchmark WER result:
+| | [VIVOS](https://huggingface.co/datasets/vivos) | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) |
+|---|---|---|---|
+|without LM| 8.23 | 12.15 | 12.15 |
+|with 4-grams LM| 3.70 | 5.57 | 5.76 |
+
+The language model was trained using [OSCAR](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109) dataset on about 32GB of crawled text.
+
+### Install SpeechBrain
+To use this model, you should install speechbrain > 0.5.10
+
+### Usage
+The model can be used directly (without a language model) as follows:
+```python
+from speechbrain.pretrained import EncoderASR
+
+model = EncoderASR.from_hparams(source="dragonSwing/wav2vec2-base-vn-270h", savedir="pretrained_models/asr-wav2vec2-vi")
+model.transcribe_file('dragonSwing/wav2vec2-base-vn-270h/example.mp3')
+# Output: được hồ chí minh coi là một động lực lớn của sự phát triển đất nước
+```
+
+### Inference on GPU
+To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.
+
+### Evaluation
+The model can be evaluated as follows on the Vietnamese test data of Common Voice 8.0.
+```python
+import torch
+import torchaudio
+from datasets import load_dataset, load_metric, Audio
+from transformers import Wav2Vec2FeatureExtractor
+from speechbrain.pretrained import EncoderASR
+import re
+test_dataset = load_dataset("mozilla-foundation/common_voice_8_0", "vi", split="test", use_auth_token=True)
+test_dataset = test_dataset.cast_column("audio", Audio(sampling_rate=16_000))
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+wer = load_metric("wer")
+extractor = Wav2Vec2FeatureExtractor.from_pretrained("dragonSwing/wav2vec2-base-vn-270h")
+model = EncoderASR.from_hparams(source="dragonSwing/wav2vec2-base-vn-270h", savedir="pretrained_models/asr-wav2vec2-vi", run_opts={'device': device})
+chars_to_ignore_regex = r'[,?.!\-;:"“%\'�]'
+# Preprocessing the datasets.
+# We need to read the audio files as arrays
+def speech_file_to_array_fn(batch):
+  audio = batch["audio"]
+  batch["target_text"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
+  batch['speech'] = audio['array']
+  return batch
+test_dataset = test_dataset.map(speech_file_to_array_fn)
+
+def evaluate(batch):
+  # For padding inputs only
+  inputs = extractor(
+    batch['speech'], 
+    sampling_rate=16000, 
+    return_tensors="pt", 
+    padding=True, 
+    do_normalize=False
+  ).input_values
+  input_lens = torch.ones(inputs.shape[0])
+  pred_str, pred_tokens = model.transcribe_batch(inputs, input_lens)
+  batch["pred_strings"] = pred_str
+  
+  return batch
+result = test_dataset.map(evaluate, batched=True, batch_size=1)
+print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["target_text"])))
+```
+**Test Result**: 12.155553%
+
+#### Citation
+```
+@misc{SB2021,
+    author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
+    title = {SpeechBrain},
+    year = {2021},
+    publisher = {GitHub},
+    journal = {GitHub repository},
+    howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
+  }
+```
+
+#### About SpeechBrain
+SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.  
+Website: [https://speechbrain.github.io](https://speechbrain.github.io/)  
+GitHub: [https://github.com/speechbrain/speechbrain](https://github.com/speechbrain/speechbrain)

config.json

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+{
+    "speechbrain_interface": "EncoderASR",
+    "activation_dropout": 0.0,
+    "apply_spec_augment": true,
+    "architectures": [
+        "Wav2Vec2Model"
+    ],
+    "attention_dropout": 0.1,
+    "bos_token_id": 1,
+    "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_dropout": 0.0,
+    "feat_extract_norm": "group",
+    "feat_proj_dropout": 0.1,
+    "feat_quantizer_dropout": 0.0,
+    "final_dropout": 0.1,
+    "hidden_act": "gelu",
+    "hidden_dropout": 0.1,
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 768,
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "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": 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,
+    "pad_token_id": 109,
+    "proj_codevector_dim": 256,
+    "torch_dtype": "float32",
+    "transformers_version": "4.6.1",
+    "vocab_size": 110
+}

custom.py

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+from transformers import Wav2Vec2CTCTokenizer
+
+class Wav2Vec2WordpieceTokenizer(Wav2Vec2CTCTokenizer):
+    def __init__(
+        self,
+        vocab_file,
+        bos_token="<s>",
+        eos_token="</s>",
+        unk_token="<unk>",
+        pad_token="<pad>",
+        word_delimiter_token="|",
+        do_lower_case=False,
+        **kwargs
+    ):
+        super().__init__(
+            vocab_file=vocab_file,
+            unk_token=unk_token,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            pad_token=pad_token,
+            do_lower_case=do_lower_case,
+            word_delimiter_token=word_delimiter_token,
+            **kwargs,
+        )
+
+        self._create_trie(self.all_special_tokens_extended)
+        
+    def _tokenize(self, text, **kwargs):
+        """
+        Converts a string in a sequence of tokens (string), using the tokenizer.
+        """
+        special_cases = set(['gia', 'qui', 'quy', 'que', 'qua'])
+        output_tokens = []
+        for token_idx, token in enumerate(text.split()):
+            if token in special_cases:
+                sub_tokens = [token[:2], token[2:]]
+            else:
+                end = len(token)
+                sub_tokens = []
+                while end > 0:
+                    start = 0
+                    cur_substr = None
+                    while start < end:
+                        substr = token[start:end]
+                        if substr in self.encoder:
+                            cur_substr = substr
+                            break
+                        start += 1
+                    if cur_substr is None:
+                        sub_tokens.insert(0, self.unk_token)
+                        end = start - 1
+                    else:
+                        sub_tokens.insert(0, cur_substr)
+                        end = start
+            
+            if token_idx > 0:
+                output_tokens.append(self.word_delimiter_token)
+            output_tokens.extend(sub_tokens)
+        return output_tokens
+    
+    def decode_ids(
+        self, 
+        token_ids, 
+        skip_special_tokens = False, 
+        clean_up_tokenization_spaces = True,
+        group_tokens: bool = True,
+        spaces_between_special_tokens: bool = False,
+    ) -> str:
+        # For compatible with speechbrain interfaces
+        return self.decode(
+            token_ids,
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            group_tokens=group_tokens,
+            spaces_between_special_tokens=spaces_between_special_tokens
+        )

hyperparams.yaml

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+# ################################
+# Model: wav2vec2 + CTC
+# Augmentation: SpecAugment
+# Authors: Le Do Thanh Binh 2021
+# ################################
+
+sample_rate: 16000
+wav2vec2_hub: dragonSwing/wav2vec2-base-vn-270h
+
+# Model parameters
+activation: !name:torch.nn.LeakyReLU
+dnn_layers: 2
+dnn_neurons: 768
+dropout_prob: 0.1
+
+# Outputs
+output_neurons: 696 # BPE size, index(blank/eos/bos) = 0
+output_tones: 12
+
+# Decoding parameters
+# Be sure that the bos and eos index match with the BPEs ones
+blank_index: 0
+bos_index: 1
+eos_index: 2
+unk_index: 3
+
+tokenizer: !apply:custom.Wav2Vec2WordpieceTokenizer.from_pretrained
+  pretrained_model_name_or_path: !ref <wav2vec2_hub>
+
+dropout: !new:torch.nn.Dropout
+  p: !ref <dropout_prob>
+
+wav2vec2: !new:speechbrain.lobes.models.huggingface_wav2vec.HuggingFaceWav2Vec2
+  source: !ref <wav2vec2_hub>
+  output_norm: True
+  freeze: True
+  save_path: model_checkpoints
+
+lm_head: !new:torch.nn.Linear
+  in_features: !ref <dnn_neurons>
+  out_features: !ref <output_neurons>
+
+tone_head: !new:torch.nn.Linear
+  in_features: !ref <dnn_neurons>
+  out_features: !ref <output_tones>
+
+log_softmax: !new:speechbrain.nnet.activations.Softmax
+  apply_log: True
+
+ctc_cost: !name:speechbrain.nnet.losses.ctc_loss
+  blank_index: !ref <blank_index>
+
+model: !new:torch.nn.ModuleList
+  - [!ref <wav2vec2>, !ref <dropout>, !ref <lm_head>, !ref <tone_head>]
+
+encoder: !new:speechbrain.nnet.containers.LengthsCapableSequential
+  wav2vec2: !ref <wav2vec2>
+  dropout: !ref <dropout>
+  lm_head: !ref <lm_head>
+
+decoding_function: !name:speechbrain.decoders.ctc_greedy_decode
+  blank_id: !ref <blank_index>
+
+modules:
+  encoder: !ref <encoder>
+
+pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
+  loadables:
+    model: !ref <model>

preprocessor_config.json

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

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": "|", "tokenizer_class": "Wav2Vec2WordpieceTokenizer"}

vocab.json

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+{ "<pad>": 0, "<s>": 1, "</s>": 2, "<unk>": 3, "|": 4, "a": 5, "á": 6, "à": 7, "ả": 8, "ã": 9, "ạ": 10, "e": 11, "é": 12, "è": 13, "ẻ": 14, "ẽ": 15, "ẹ": 16, "ê": 17, "ế": 18, "ề": 19, "ể": 20, "ễ": 21, "ệ": 22, "i": 23, "í": 24, "ì": 25, "ỉ": 26, "ĩ": 27, "ị": 28, "o": 29, "ó": 30, "ò": 31, "ỏ": 32, "õ": 33, "ọ": 34, "ơ": 35, "ớ": 36, "ờ": 37, "ở": 38, "ỡ": 39, "ợ": 40, "ô": 41, "ố": 42, "ồ": 43, "ổ": 44, "ỗ": 45, "ộ": 46, "u": 47, "ú": 48, "ù": 49, "ủ": 50, "ũ": 51, "ụ": 52, "ư": 53, "ứ": 54, "ừ": 55, "ử": 56, "ữ": 57, "ự": 58, "y": 59, "ỳ": 60, "ý": 61, "ỷ": 62, "ỹ": 63, "ỵ": 64, "ă": 65, "ắ": 66, "ằ": 67, "ẳ": 68, "ẵ": 69, "ặ": 70, "â": 71, "ấ": 72, "ầ": 73, "ẩ": 74, "ẫ": 75, "ậ": 76, "đ": 77, "q": 78, "w": 79, "r": 80, "t": 81, "p": 82, "s": 83, "d": 84, "f": 85, "g": 86, "h": 87, "j": 88, "k": 89, "l": 90, "z": 91, "x": 92, "c": 93, "v": 94, "b": 95, "n": 96, "m": 97, "th": 98, "ch": 99, "kh": 100, "ph": 101, "nh": 102, "gh": 103, "qu": 104, "ng": 105, "ngh": 106, "tr": 107, "ác": 108, "ạc": 109, "ai": 110, "ái": 111, "ài": 112, "ải": 113, "ãi": 114, "ại": 115, "am": 116, "ám": 117, "àm": 118, "ảm": 119, "ãm": 120, "ạm": 121, "an": 122, "án": 123, "àn": 124, "ản": 125, "ãn": 126, "ạn": 127, "ao": 128, "áo": 129, "ào": 130, "ảo": 131, "ão": 132, "ạo": 133, "au": 134, "áu": 135, "àu": 136, "ảu": 137, "ãu": 138, "ạu": 139, "áp": 140, "ạp": 141, "át": 142, "ạt": 143, "ay": 144, "áy": 145, "ày": 146, "ảy": 147, "ãy": 148, "ạy": 149, "ắc": 150, "ặc": 151, "ăm": 152, "ằm": 153, "ắm": 154, "ẳm": 155, "ẵm": 156, "ặm": 157, "ăn": 158, "ắn": 159, "ằn": 160, "ẳn": 161, "ẵn": 162, "ặn": 163, "ắp": 164, "ặp": 165, "ắt": 166, "ặt": 167, "ấc": 168, "ậc": 169, "âm": 170, "ấm": 171, "ầm": 172, "ẩm": 173, "ẫm": 174, "ậm": 175, "ân": 176, "ấn": 177, "ần": 178, "ẩn": 179, "ẫn": 180, "ận": 181, "ấp": 182, "ập": 183, "ất": 184, "ật": 185, "âu": 186, "ấu": 187, "ầu": 188, "ẩu": 189, "ẫu": 190, "ậu": 191, "ây": 192, "ấy": 193, "ầy": 194, "ẩy": 195, "ẫy": 196, "ậy": 197, "éc": 198, 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