this is firenetixWhisper V3

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README.md

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+---
+language: vi
+datasets:
+- vlsp
+- vivos
+tags:
+- audio
+- automatic-speech-recognition
+license: cc-by-nc-4.0
+widget:
+- example_title: VLSP ASR 2020 test T1
+  src: https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/raw/main/audio-test/t1_0001-00010.wav
+- example_title: VLSP ASR 2020 test T1
+  src: https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/raw/main/audio-test/t1_utt000000042.wav
+- example_title: VLSP ASR 2020 test T2
+  src: https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/raw/main/audio-test/t2_0000006682.wav
+model-index:
+- name: Vietnamese end-to-end speech recognition using wav2vec 2.0 by VietAI
+  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: 11.52
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: VIVOS
+      type: vivos
+      args: vi
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 6.15
+---
+
+# Vietnamese end-to-end speech recognition using wav2vec 2.0
+
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/vietnamese-end-to-end-speech-recognition/speech-recognition-on-common-voice-vi)](https://paperswithcode.com/sota/speech-recognition-on-common-voice-vi?p=vietnamese-end-to-end-speech-recognition)
+
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/vietnamese-end-to-end-speech-recognition/speech-recognition-on-vivos)](https://paperswithcode.com/sota/speech-recognition-on-vivos?p=vietnamese-end-to-end-speech-recognition)
+
+
+[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
+
+### Model description
+
+[Our models](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h) are pre-trained on 13k hours of Vietnamese youtube audio (un-label data) and fine-tuned on 250 hours labeled of [VLSP ASR dataset](https://vlsp.org.vn/vlsp2020/eval/asr) on 16kHz sampled speech audio. 
+
+We use [wav2vec2 architecture](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) for the pre-trained model. Follow wav2vec2 paper:
+
+>For the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler.
+
+For fine-tuning phase, wav2vec2 is fine-tuned using Connectionist Temporal Classification (CTC), which is an algorithm that is used to train neural networks for sequence-to-sequence problems and mainly in Automatic Speech Recognition and handwriting recognition.
+
+| Model | #params | Pre-training data | Fine-tune data |
+|---|---|---|---|
+| [base]((https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h)) | 95M | 13k hours | 250 hours |
+
+In a formal ASR system, two components are required: acoustic model and language model. Here ctc-wav2vec fine-tuned model works as an acoustic model. For the language model, we provide a [4-grams model](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/blob/main/vi_lm_4grams.bin.zip) trained on 2GB of spoken text. 
+
+Detail of training and fine-tuning process, the audience can follow [fairseq github](https://github.com/pytorch/fairseq/tree/master/examples/wav2vec) and [huggingface blog](https://huggingface.co/blog/fine-tune-wav2vec2-english).
+
+### Benchmark WER result:
+
+| | [VIVOS](https://ailab.hcmus.edu.vn/vivos) | [COMMON VOICE VI](https://paperswithcode.com/dataset/common-voice) | [VLSP-T1](https://vlsp.org.vn/vlsp2020/eval/asr) | [VLSP-T2](https://vlsp.org.vn/vlsp2020/eval/asr) |
+|---|---|---|---|---|
+|without LM| 10.77 | 18.34 | 13.33 | 51.45 |
+|with 4-grams LM| 6.15 | 11.52 | 9.11 | 40.81 |
+
+
+### Example usage
+
+When using the model make sure that your speech input is sampled at 16Khz. Audio length should be shorter than 10s. Following the Colab link below to use a combination of CTC-wav2vec and 4-grams LM.
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1pVBY46gSoWer2vDf0XmZ6uNV3d8lrMxx?usp=sharing)
+
+
+```python
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+from datasets import load_dataset
+import soundfile as sf
+import torch
+
+# load model and tokenizer
+processor = Wav2Vec2Processor.from_pretrained("nguyenvulebinh/wav2vec2-base-vietnamese-250h")
+model = Wav2Vec2ForCTC.from_pretrained("nguyenvulebinh/wav2vec2-base-vietnamese-250h")
+
+# define function to read in sound file
+def map_to_array(batch):
+    speech, _ = sf.read(batch["file"])
+    batch["speech"] = speech
+    return batch
+
+# load dummy dataset and read soundfiles
+ds = map_to_array({
+    "file": 'audio-test/t1_0001-00010.wav'
+})
+
+# tokenize
+input_values = processor(ds["speech"], return_tensors="pt", padding="longest").input_values  # Batch size 1
+
+# retrieve logits
+logits = model(input_values).logits
+
+# take argmax and decode
+predicted_ids = torch.argmax(logits, dim=-1)
+transcription = processor.batch_decode(predicted_ids)
+ ```
+
+### Model Parameters License
+
+The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: https://creativecommons.org/licenses/by-nc/4.0/legalcode
+
+### Citation
+
+[![CITE](https://zenodo.org/badge/DOI/10.5281/zenodo.5356039.svg)](https://github.com/vietai/ASR)
+
+```text
+@misc{Thai_Binh_Nguyen_wav2vec2_vi_2021,
+  author = {Thai Binh Nguyen},
+  doi = {10.5281/zenodo.5356039},
+  month = {09},
+  title = {{Vietnamese end-to-end speech recognition using wav2vec 2.0}},
+  url = {https://github.com/vietai/ASR},
+  year = {2021}
+}
+```
+**Please CITE** our repo when it is used to help produce published results or is incorporated into other software.
+
+# Contact 
+
+nguyenvulebinh@gmail.com / binh@vietai.org
+
+[![Follow](https://img.shields.io/twitter/follow/nguyenvulebinh?style=social)](https://twitter.com/intent/follow?screen_name=nguyenvulebinh)

config.json

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+{
+  "_name_or_path": "/content/vaw2tmp/model-bin/finetune/base/checkpoint-146596",
+  "activation_dropout": 0.1,
+  "apply_spec_augment": true,
+  "architectures": [
+    "Wav2Vec2ForCTC"
+  ],
+  "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,
+  "gradient_checkpointing": true,
+  "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": "4.9.2",
+  "vocab_size": 110
+}

model.bin

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+oid sha256:2529ffc057c90b3d576ea109999ea49f3bab9691f7a93297a91d69ce8c981f84
+size 377906903

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": "Wav2Vec2CTCTokenizer"}

vi_lm_4grams.bin.zip

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+oid sha256:5d557b72b536a3c934b5d277b0263677ea4d32a739f44f4050e51cd197a972b3
+size 903963528

vocab.json

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+{"ẻ": 0, "6": 1, "ụ": 2, "í": 3, "3": 4, "ỹ": 5, "ý": 6, "ẩ": 7, "ở": 8, "ề": 9, "õ": 10, "7": 11, "ê": 12, "ứ": 13, "ỏ": 14, "v": 15, "ỷ": 16, "a": 17, "l": 18, "ự": 19, "q": 20, "ờ": 21, "j": 22, "ố": 23, "à": 24, "ỗ": 25, "n": 26, "é": 27, "ủ": 28, "у": 29, "ô": 30, "u": 31, "y": 32, "ằ": 33, "4": 34, "w": 35, "b": 36, "ệ": 37, "ễ": 38, "s": 39, "ì": 40, "ầ": 41, "ỵ": 42, "8": 43, "d": 44, "ể": 45, "r": 47, "ũ": 48, "c": 49, "ạ": 50, "9": 51, "ế": 52, "ù": 53, "ỡ": 54, "2": 55, "t": 56, "i": 57, "g": 58, "́": 59, "ử": 60, "̀": 61, "á": 62, "0": 63, "ậ": 64, "e": 65, "ộ": 66, "m": 67, "ẳ": 68, "ợ": 69, "ĩ": 70, "h": 71, "â": 72, "ú": 73, "ọ": 74, "ồ": 75, "ặ": 76, "f": 77, "ữ": 78, "ắ": 79, "ỳ": 80, "x": 81, "ó": 82, "ã": 83, "ổ": 84, "ị": 85, "̣": 86, "z": 87, "ả": 88, "đ": 89, "è": 90, "ừ": 91, "ò": 92, "ẵ": 93, "1": 94, "ơ": 95, "k": 96, "ẫ": 97, "p": 98, "ấ": 99, "ẽ": 100, "ỉ": 101, "ớ": 102, "ẹ": 103, "ă": 104, "o": 105, "ư": 106, "5": 107, "|": 46, "<unk>": 108, "<pad>": 109}