wav2vec2 (ar)

.gitattributes

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+ar/Arabic_speech_Syllables_recognition_Using_Wav2vec2/Final[[:space:]]Paper[[:space:]]Syllable-Based[[:space:]]Arabic[[:space:]]Speech[[:space:]]Recognition[[:space:]]Using[[:space:]]Wav2Vec.pdf filter=lfs diff=lfs merge=lfs -text
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+ar/wav2vec2_ar_anz2/language_model/voctext.arpa filter=lfs diff=lfs merge=lfs -text

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ar/Arabic_speech_Syllables_recognition_Using_Wav2vec2/Final Paper Syllable-Based Arabic Speech Recognition Using Wav2Vec.pdf

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+oid sha256:f8ecbb1a138bcb803d90e40c18c4617a8bef710a84d13793afcf5eb89f8f4ebe
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ar/Arabic_speech_Syllables_recognition_Using_Wav2vec2/README.md

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+---
+datasets:
+- IbrahimSalah/The_Arabic_News_speech_Corpus_Dataset
+language:
+- ar
+tags:
+- Arabic
+- MSA
+- Speech
+- Syllables
+- Wav2vec
+- ASR
+---
+ # Arabic syllables recognition with tashkeel
+ **paper DOI** : https://doi.org/10.60161/2521-001-001-006 \
+This is fine tuned wav2vec2 model to recognize arabic syllables from speech.        
+The model was trained on Modern standard arabic dataset .\
+5-gram language model is available with the model.
+
+To try it out :
+
+```
+!pip install datasets transformers
+!pip install https://github.com/kpu/kenlm/archive/master.zip pyctcdecode
+```
+
+```
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+from transformers import Wav2Vec2ProcessorWithLM
+processor = Wav2Vec2ProcessorWithLM.from_pretrained('IbrahimSalah/Syllables_final_Large')
+model = Wav2Vec2ForCTC.from_pretrained("IbrahimSalah/Syllables_final_Large")
+```
+```
+import pandas as pd
+dftest = pd.DataFrame(columns=['audio'])
+import datasets
+from datasets import Dataset
+path ='/content/908-33.wav'
+dftest['audio']=[path]  ## audio path
+dataset = Dataset.from_pandas(dftest)
+```
+```
+import torch
+import torchaudio
+def speech_file_to_array_fn(batch):
+    speech_array, sampling_rate = torchaudio.load(batch["audio"])
+    print(sampling_rate)
+    resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) # The original data was with 48,000 sampling rate. You can change it according to your input.
+    batch["audio"] = resampler(speech_array).squeeze().numpy()
+    return batch
+```
+```
+import numpy as np
+from datasets import load_dataset
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+test_dataset = dataset.map(speech_file_to_array_fn)
+inputs = processor(test_dataset["audio"], sampling_rate=16_000, return_tensors="pt", padding=True)
+with torch.no_grad():
+    logits = model(inputs.input_values).logits
+    print(logits.numpy().shape)
+
+transcription = processor.batch_decode(logits.numpy()).text
+print("Prediction:",transcription[0])
+```
+
+
+# You can then convert the syllables to full word using our fine tuned mT5 model[IbrahimSalah/Arabic_Syllables_to_text_Converter_Using_MT5]
+
+## Citation
+
+**BibTeX:**
+
+```bibtex
+@article{2024SyllableBasedAS,
+  title={Syllable-Based Arabic Speech Recognition Using Wav2Vec},
+  author={إبراهيم عبدالعال and مصطفى الشافعي and محمد عبدالواحد},
+  journal={مجلة اللغات الحاسوبية والمعالجة الآلية للغة العربية},
+  year={2024},
+  url={https://api.semanticscholar.org/CorpusID:269151543}
+}

ar/Arabic_speech_Syllables_recognition_Using_Wav2vec2/added_tokens.json

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+{
+
+}

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ar/Arabic_speech_Syllables_recognition_Using_Wav2vec2/config.json

@@ -0,0 +1,117 @@
+{
+  "_name_or_path": "/kaggle/working/speech_folder_5/checkpoint-54500",
+  "activation_dropout": 0.0,
+  "adapter_attn_dim": null,
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+  "conv_bias": true,
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+  "ctc_loss_reduction": "mean",
+  "ctc_zero_infinity": false,
+  "diversity_loss_weight": 0.1,
+  "do_stable_layer_norm": true,
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
+  "feat_extract_dropout": 0.0,
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+  "final_dropout": 0.0,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
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+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.0001,
+  "mask_channel_length": 10,
+  "mask_channel_min_space": 1,
+  "mask_channel_other": 0.0,
+  "mask_channel_prob": 0.0,
+  "mask_channel_selection": "static",
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+  "mask_feature_min_masks": 0,
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+  "model_type": "wav2vec2",
+  "num_adapter_layers": 3,
+  "num_attention_heads": 16,
+  "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": 24,
+  "num_negatives": 100,
+  "output_hidden_size": 1024,
+  "pad_token_id": 3084,
+  "proj_codevector_dim": 768,
+  "tdnn_dilation": [
+    1,
+    2,
+    3,
+    1,
+    1
+  ],
+  "tdnn_dim": [
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+    512,
+    512,
+    512,
+    1500
+  ],
+  "tdnn_kernel": [
+    5,
+    3,
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+    1,
+    1
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.30.2",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 3086,
+  "xvector_output_dim": 512
+}

ar/Arabic_speech_Syllables_recognition_Using_Wav2vec2/language_model/6gram.bin

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ar/Arabic_speech_Syllables_recognition_Using_Wav2vec2/language_model/attrs.json

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ar/Arabic_speech_Syllables_recognition_Using_Wav2vec2/language_model/unigrams.txt

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+  "ھِيْمْ": 1980,
+  "ھِيْنْ": 49,
+  "ھِيْي": 2116
+}

ar/Wav2vecLarge_quran_syllables_recognition/.gitattributes

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ar/Wav2vecLarge_quran_syllables_recognition/README.md

@@ -0,0 +1,80 @@
+---
+language:
+- ar
+metrics:
+- wer
+- cer
+tags:
+- Quran
+- speech
+- arabic
+- asr
+---
+ # Quran syllables recognition with tashkeel.
+This is fine tuned wav2vec2 model to recognize quran syllables from speech.        
+The model was trained on private dataset along with part of Tarteel dataset after cleanning and converting into syllables .\
+5-gram language model is available with the model.
+
+The model transcripe audio speech into syllables .\
+For instance, when presented with the audio and transcription "مِنَ الْجِنَّةِ وَالنَّاسِ" the expected model output would be
+"مِ نَلْ جِنْ نَ تِ وَنْ نَاْسْ" .\
+To try it out :
+
+```
+!pip install datasets transformers
+!pip install https://github.com/kpu/kenlm/archive/master.zip pyctcdecode
+```
+
+```
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+from transformers import Wav2Vec2ProcessorWithLM
+processor = Wav2Vec2ProcessorWithLM.from_pretrained('IbrahimSalah/Wav2vecLarge_quran_syllables_recognition')
+model = Wav2Vec2ForCTC.from_pretrained("IbrahimSalah/Wav2vecLarge_quran_syllables_recognition")
+```
+```
+import pandas as pd
+dftest = pd.DataFrame(columns=['audio'])
+import datasets
+from datasets import Dataset
+path ='/content/908-33.wav'
+dftest['audio']=[path]  ## audio path
+dataset = Dataset.from_pandas(dftest)
+```
+```
+import torch
+import torchaudio
+def speech_file_to_array_fn(batch):
+    speech_array, sampling_rate = torchaudio.load(batch["audio"])
+    print(sampling_rate)
+    resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) # The original data was with 48,000 sampling rate. You can change it according to your input.
+    batch["audio"] = resampler(speech_array).squeeze().numpy()
+    return batch
+```
+```
+import numpy as np
+from datasets import load_dataset
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+test_dataset = dataset.map(speech_file_to_array_fn)
+inputs = processor(test_dataset["audio"], sampling_rate=16_000, return_tensors="pt", padding=True)
+with torch.no_grad():
+    logits = model(inputs.input_values).logits
+    print(logits.numpy().shape)
+
+transcription = processor.batch_decode(logits.numpy()).text
+print("Prediction:",transcription[0])
+```
+
+You can try the model with live recording using this Google Colab notebook : [Live Recording Recognition](https://colab.research.google.com/drive/1WYFG03o93-CBFNHhAuAo3MNmzgo4nLEJ?usp=sharing)
+
+
+sample audios and Outputs
+
+1- <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/645098004f731658826cfe57/tZLR9sn6VnsjYS5xT2qFB.wav"></audio>
+
+2- <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/645098004f731658826cfe57/_6p54hi5JRt_PIqxY-P0I.wav"></audio>
+
+Output
+```
+1- ءُوْ لَاْ ءِ كَ لَمْ يَ كُوْ نُوْ مُعْ جِ زِيْ نَ فِلْ ءَرْ ضِ وَ مَاْ كَاْ نَ لَ ھُمْ مِنْ ءَوْ لِ يَاْ
+2- ءِذْ قَاْ لَ يُوْ سُ فُ لِ ءَيْ بِيْ ھِ يَاْ ءَ بَ تِ ءِنْ نِيْ رَ ءَيْ تُ ءَ حَ دَ عَ شَ رَ كَوْ كَ بَلْ وَشْ شَمْ سَ وَلْ قَ مَ ضَ رَ ءَيْ تُ ھُمْ لِيْ سَاْ جِ دِيْنْ
+```

ar/Wav2vecLarge_quran_syllables_recognition/alphabet.json

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ar/Wav2vecLarge_quran_syllables_recognition/config.json

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+{
+  "_name_or_path": "speech_out21/checkpoint-7000",
+  "activation_dropout": 0.1,
+  "adapter_attn_dim": null,
+  "adapter_kernel_size": 3,
+  "adapter_stride": 2,
+  "add_adapter": false,
+  "apply_spec_augment": true,
+  "architectures": [
+    "Wav2Vec2ForCTC"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "classifier_proj_size": 256,
+  "codevector_dim": 768,
+  "contrastive_logits_temperature": 0.1,
+  "conv_bias": true,
+  "conv_dim": [
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+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
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+    2,
+    2
+  ],
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+    5,
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+    2,
+    2,
+    2,
+    2,
+    2
+  ],
+  "ctc_loss_reduction": "mean",
+  "ctc_zero_infinity": false,
+  "diversity_loss_weight": 0.1,
+  "do_stable_layer_norm": true,
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
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+  "feat_extract_norm": "layer",
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+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
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+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
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+  "mask_time_min_masks": 2,
+  "mask_time_min_space": 1,
+  "mask_time_other": 0.0,
+  "mask_time_prob": 0.1,
+  "mask_time_selection": "static",
+  "model_type": "wav2vec2",
+  "num_adapter_layers": 3,
+  "num_attention_heads": 16,
+  "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": 24,
+  "num_negatives": 100,
+  "output_hidden_size": 1024,
+  "pad_token_id": 2075,
+  "proj_codevector_dim": 768,
+  "tdnn_dilation": [
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+    512,
+    1500
+  ],
+  "tdnn_kernel": [
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+    1,
+    1
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+  "torch_dtype": "float32",
+  "transformers_version": "4.39.0.dev0",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 2076,
+  "xvector_output_dim": 512
+}

ar/Wav2vecLarge_quran_syllables_recognition/datasetMetadata.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<dataset_metadata>
+  <title>Quran Speech Recognition Dataset</title>
+  <description>
+    This dataset is designed for training and evaluating Quranic speech recognition models, with a focus on syllable-based transcription. It includes audio recordings and corresponding syllabized transcriptions from professional reciters and regular users, ensuring coverage of real-world scenarios. 
+  </description>
+  <creator>Research Group for Quranic Speech Recognition</creator>
+  <contributors>
+    <contributor>Tarek ELDEEB</contributor>
+    <contributor>Dr.Moustafa Elshafi - Zewailcity</contributor>
+  </contributors>
+  <license>Creative Commons Attribution 4.0 International (CC BY 4.0)</license>
+  <languages>Arabic (ar)</languages>
+  <dataset_link>https://archive.org/details/quran-speech-dataset</dataset_link>
+  <torrent_file>
+    <complete_dataset_torrent>https://utweb.rainberrytv.com/gui/share.html#link=magnet%3A%3Fxt%3Durn%3Abtih%3A34e50d0fd9afb7f308883b14e5e60f6532e30141%26dn%3Dquran-speech-dataset%26ws%3Dhttp%253a%252f%252fia601500.us.archive.org%252f23%252fitems%252f%26tr%3Dhttp%253a%252f%252fbt1.archive.org%253a6969%252fannounce%26tr%3Dhttp%253a%252f%252fbt2.archive.org%253a6969%252fannounce</complete_dataset_torrent>
+  </torrent_file>
+  <train_set>
+    <number_of_samples>11004</number_of_samples>
+    <total_duration_seconds>63691.96</total_duration_seconds>
+    <total_duration_hours>17.69</total_duration_hours>
+    <maximum_audio_duration>24.71</maximum_audio_duration>
+    <sampling_rate>16000 Hz</sampling_rate>
+    <bit_depth>16-bit</bit_depth>
+    <channels>Mono (1)</channels>
+    <notes>
+      Audio recordings from professional Quranic reciters, syllabized using automated Tajweed rule-based software. Preprocessing steps included text normalization and resampling to match the required format.
+    </notes>
+  </train_set>
+  <test_set>
+    <number_of_samples>2823</number_of_samples>
+    <total_duration_seconds>21161.93</total_duration_seconds>
+    <total_duration_hours>5.88</total_duration_hours>
+    <maximum_audio_duration>24.83</maximum_audio_duration>
+    <sampling_rate>16000 Hz</sampling_rate>
+    <bit_depth>16-bit</bit_depth>
+    <channels>Mono (1)</channels>
+    <composition>
+      <professional_reciters>1169</professional_reciters>
+      <regular_users>1654</regular_users>
+    </composition>
+    <notes>
+      Test set includes a mix of professional reciters and regular users to simulate real-world usage scenarios. Transcriptions were syllabized using automated software based on Tajweed rules.
+    </notes>
+  </test_set>
+  <processing_notes>
+    <text_normalization>
+      Removed unnecessary characters and ensured syllable alignment with Quranic Tajweed rules.
+    </text_normalization>
+    <audio_resampling>
+      Resampled all audio files to 16 kHz to match the Wav2Vec 2.0 pre-trained model requirements.
+    </audio_resampling>
+  </processing_notes>
+</dataset_metadata>

ar/Wav2vecLarge_quran_syllables_recognition/language_model/5gram.bin

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+oid sha256:63bdd3bc1578d2d95f5404b9cecda0bb96940c7e082e900f43a6e25d027c695f
+size 8070657

ar/Wav2vecLarge_quran_syllables_recognition/language_model/attrs.json

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+{"alpha": 0.3, "beta": 1.0, "unk_score_offset": -10.0, "score_boundary": true}

ar/Wav2vecLarge_quran_syllables_recognition/language_model/unigrams.txt

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ar/Wav2vecLarge_quran_syllables_recognition/model.safetensors

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+size 1270319104

ar/Wav2vecLarge_quran_syllables_recognition/preprocessor_config.json

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ar/Wav2vecLarge_quran_syllables_recognition/source.txt

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ar/Wav2vecLarge_quran_syllables_recognition/special_tokens_map.json

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ar/Wav2vecLarge_quran_syllables_recognition/vocab.json

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ar/XLSR-Wav2Vec2-arabic/README.md

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+---
+datasets:
+- mozilla-foundation/common_voice_17_0
+- tunis-ai/arabic_speech_corpus
+language:
+- ar
+metrics:
+- wer
+- cer
+pipeline_tag: automatic-speech-recognition
+license: apache-2.0
+---

ar/XLSR-Wav2Vec2-arabic/all_results.json

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+{
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+}

ar/XLSR-Wav2Vec2-arabic/config.json

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ar/asr-wav2vec2-commonvoice-14-ar/README.md

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+---
+language:
+- ar
+thumbnail: null
+pipeline_tag: automatic-speech-recognition
+tags:
+- CTC
+- pytorch
+- speechbrain
+- Transformer
+license: apache-2.0
+datasets:
+- commonvoice.14.0
+metrics:
+- wer
+- cer
+model-index:
+- name: asr-wav2vec2-commonvoice-14-ar
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: CommonVoice Corpus 14.0 (Arabic)
+      type: mozilla-foundation/common_voice_14.0
+      config: ar
+      split: test
+      args:
+        language: ar
+    metrics:
+    - name: Test WER
+      type: wer
+      value: '29.92'
+---
+
+<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
+<br/><br/>
+
+# wav2vec 2.0 with CTC trained on CommonVoice Arabic (No LM)
+
+This repository provides all the necessary tools to perform automatic speech
+recognition from an end-to-end system pretrained on CommonVoice (Arabic Language) within
+SpeechBrain. For a better experience, we encourage you to learn more about
+[SpeechBrain](https://speechbrain.github.io).
+
+The performance of the model is the following:
+
+| Release | Test CER | Test WER | GPUs |
+|:-------------:|:--------------:|:--------------:| :--------:|
+| 15-08-23 | 10.01  | 29.92  | 1xV100 32GB |
+
+## Pipeline description
+
+This ASR system is composed of 2 different but linked blocks:
+- Tokenizer (unigram) that transforms words into unigrams and trained with
+the train transcriptions (train.tsv) of CommonVoice (ar).  
+- Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model (wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)) is combined with two DNN layers and finetuned on CommonVoice DE. 
+The obtained final acoustic representation is given to the CTC decoder.
+
+The system is trained with recordings sampled at 16kHz (single channel).
+The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed.
+
+## Install SpeechBrain
+
+First of all, please install tranformers and SpeechBrain with the following command:
+
+```
+pip install speechbrain transformers
+```
+
+Please notice that we encourage you to read our tutorials and learn more about
+[SpeechBrain](https://speechbrain.github.io).
+
+### Transcribing your own audio files (in Arabic)
+
+```python
+from speechbrain.inference.ASR import EncoderASR
+
+asr_model = EncoderASR.from_hparams(source="speechbrain/asr-wav2vec2-commonvoice-14-ar", savedir="pretrained_models/asr-wav2vec2-commonvoice-14-ar")
+asr_model.transcribe_file("speechbrain/asr-wav2vec2-commonvoice-14-ar/example-ar.wav")
+
+```
+### Inference on GPU
+To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.
+
+## Parallel Inference on a Batch
+Please, [see this Colab notebook](https://colab.research.google.com/drive/1hX5ZI9S4jHIjahFCZnhwwQmFoGAi3tmu?usp=sharing) to figure out how to transcribe in parallel a batch of input sentences using a pre-trained model.
+
+### Training
+The model was trained with SpeechBrain.
+To train it from scratch follow these steps:
+1. Clone SpeechBrain:
+```bash
+git clone https://github.com/speechbrain/speechbrain/
+```
+2. Install it:
+```bash
+cd speechbrain
+pip install -r requirements.txt
+pip install -e .
+```
+
+3. Run Training:
+```bash
+cd recipes/CommonVoice/ASR/CTC/
+python train_with_wav2vec.py hparams/train_ar_with_wav2vec.yaml --data_folder=your_data_folder
+```
+
+You can find our training results (models, logs, etc) [here](https://www.dropbox.com/sh/7tnuqqbr4vy96cc/AAA_5_R0RmqFIiyR0o1nVS4Ia?dl=0).
+
+### Limitations
+The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
+
+
+# **About SpeechBrain**
+- Website: https://speechbrain.github.io/
+- Code: https://github.com/speechbrain/speechbrain/
+- HuggingFace: https://huggingface.co/speechbrain/
+
+
+# **Citing SpeechBrain**
+Please, cite SpeechBrain if you use it for your research or business.
+
+```bibtex
+@misc{speechbrain,
+  title={{SpeechBrain}: A General-Purpose Speech Toolkit},
+  author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
+  year={2021},
+  eprint={2106.04624},
+  archivePrefix={arXiv},
+  primaryClass={eess.AS},
+  note={arXiv:2106.04624}
+}
+```

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+    ],
+    "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": "4.21.1",
+    "vocab_size": 32
+}

ar/asr-wav2vec2-commonvoice-14-ar/example-ar.wav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:14e94ab94ada53864d761f333561f227dadaa0bda124bbbe7250aa56911e02ef
+size 323968

ar/asr-wav2vec2-commonvoice-14-ar/hyperparams.yaml

@@ -0,0 +1,91 @@
+# ################################
+# Model: wav2vec2 + DNN + CTC
+# Augmentation: SpecAugment
+# Authors: 
+# Sung-Lin Yeh 2021
+# Pooneh Mousavi 2023
+# ################################
+
+# BPE parameters
+token_type: unigram  # ["unigram", "bpe", "char"]
+character_coverage: 1.0
+
+# Model parameters
+# activation: !name:torch.nn.LeakyReLU
+dnn_neurons: 1024
+wav2vec_output_dim: 1024
+dropout: 0.15
+
+sample_rate: 16000
+
+wav2vec2_hub: facebook/wav2vec2-large-xlsr-53
+
+# Outputs
+output_neurons: 1000  # BPE size, index(blank/eos/bos) = 0
+
+# Decoding parameters
+# Be sure that the bos and eos index match with the BPEs ones
+blank_index: 0
+bos_index: 1
+eos_index: 2
+
+enc: !new:speechbrain.nnet.containers.Sequential
+  input_shape: [null, null, !ref <wav2vec_output_dim>]
+  linear1: !name:speechbrain.nnet.linear.Linear
+    n_neurons: !ref <dnn_neurons>
+    bias: True
+  bn1: !name:speechbrain.nnet.normalization.BatchNorm1d
+  activation: !new:torch.nn.LeakyReLU
+  drop: !new:torch.nn.Dropout
+    p: !ref <dropout>
+  linear2: !name:speechbrain.nnet.linear.Linear
+    n_neurons: !ref <dnn_neurons>
+    bias: True
+  bn2: !name:speechbrain.nnet.normalization.BatchNorm1d
+  activation2: !new:torch.nn.LeakyReLU
+  drop2: !new:torch.nn.Dropout
+    p: !ref <dropout>
+  linear3: !name:speechbrain.nnet.linear.Linear
+    n_neurons: !ref <dnn_neurons>
+    bias: True
+  bn3: !name:speechbrain.nnet.normalization.BatchNorm1d
+  activation3: !new:torch.nn.LeakyReLU
+
+wav2vec2: !new:speechbrain.lobes.models.huggingface_transformers.wav2vec2.Wav2Vec2
+  source: !ref <wav2vec2_hub>
+  output_norm: True
+  freeze: True
+  save_path: wav2vec2_checkpoint
+
+ctc_lin: !new:speechbrain.nnet.linear.Linear
+  input_size: !ref <dnn_neurons>
+  n_neurons: !ref <output_neurons>
+
+log_softmax: !new:speechbrain.nnet.activations.Softmax
+  apply_log: True
+
+ctc_cost: !name:speechbrain.nnet.losses.ctc_loss
+  blank_index: !ref <blank_index>
+
+asr_model: !new:torch.nn.ModuleList
+    - [!ref <enc>, !ref <ctc_lin>]
+
+tokenizer: !new:sentencepiece.SentencePieceProcessor
+
+encoder: !new:speechbrain.nnet.containers.LengthsCapableSequential
+    wav2vec2: !ref <wav2vec2>
+    enc: !ref <enc>
+    ctc_lin: !ref <ctc_lin>
+    log_softmax: !ref <log_softmax>
+
+modules:
+  encoder: !ref <encoder>
+
+decoding_function: !name:speechbrain.decoders.ctc_greedy_decode
+    blank_id: !ref <blank_index>
+
+pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
+   loadables:
+      wav2vec2: !ref <wav2vec2>
+      asr: !ref <asr_model>
+      tokenizer: !ref <tokenizer>

ar/asr-wav2vec2-commonvoice-14-ar/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
+}