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README.md ADDED
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+ ---
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+ language: ar
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+ datasets:
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+ - common_voice
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+ metrics:
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+ - wer
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+ - cer
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+ tags:
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+ - audio
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+ - automatic-speech-recognition
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+ - speech
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+ - xlsr-fine-tuning-week
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+ license: apache-2.0
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+ model-index:
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+ - name: XLSR Wav2Vec2 Arabic by Jonatas Grosman
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+ results:
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+ - task:
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+ name: Speech Recognition
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+ type: automatic-speech-recognition
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+ dataset:
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+ name: Common Voice ar
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+ type: common_voice
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+ args: ar
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+ metrics:
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+ - name: Test WER
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+ type: wer
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+ value: 40.52
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+ - name: Test CER
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+ type: cer
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+ value: 18.37
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+ ---
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+
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+ # Wav2Vec2-Large-XLSR-53-Arabic
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+
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+ Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Arabic using the [Common Voice](https://huggingface.co/datasets/common_voice).
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+ When using this model, make sure that your speech input is sampled at 16kHz.
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+
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+ The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
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+
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+ ## Usage
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+
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+ The model can be used directly (without a language model) as follows:
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+
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+ ```python
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+ import torch
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+ import librosa
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+ from datasets import load_dataset
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+ from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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+
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+ LANG_ID = "ar"
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+ MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-arabic"
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+ SAMPLES = 5
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+
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+ test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
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+
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+ processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
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+ model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
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+
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+ # Preprocessing the datasets.
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+ # We need to read the audio files as arrays
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+ def speech_file_to_array_fn(batch):
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+ speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
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+ batch["speech"] = speech_array
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+ batch["sentence"] = batch["sentence"].upper()
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+ return batch
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+
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+ test_dataset = test_dataset.map(speech_file_to_array_fn)
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+ inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
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+
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+ with torch.no_grad():
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+ logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
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+
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+ predicted_ids = torch.argmax(logits, dim=-1)
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+ predicted_sentences = processor.batch_decode(predicted_ids)
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+
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+ for i, predicted_sentence in enumerate(predicted_sentences):
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+ print("-" * 100)
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+ print("Reference:", test_dataset[i]["sentence"])
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+ print("Prediction:", predicted_sentence)
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+ ```
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+
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+ | Reference | Prediction |
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+ | ------------- | ------------- |
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+ | ألديك قلم ؟ | ألديك قلم |
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+ | ليست هناك مسافة على هذه الأرض أبعد من يوم أمس. | ليست لنارك مسافة على هذه الأرض أبعد من يوم الأمس |
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+ | إنك تكبر المشكلة. | إنك تكبر المشكلة ك |
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+ | يرغب أن يلتقي بك. | يرغب أن يلتقي بك ن |
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+ | إنهم لا يعرفون لماذا حتى. | إنهم لا يعرفون لماذا حتى |
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+
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+ ## Evaluation
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+
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+ The model can be evaluated as follows on the Arabic test data of Common Voice.
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+
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+ ```python
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+ import torch
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+ import re
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+ import librosa
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+ from datasets import load_dataset, load_metric
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+ from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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+
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+ LANG_ID = "ar"
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+ MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-arabic"
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+ DEVICE = "cuda"
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+
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+ CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
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+ "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
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+ "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。"]
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+
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+ test_dataset = load_dataset("common_voice", LANG_ID, split="test")
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+
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+ wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py
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+ cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py
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+
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+ chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
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+
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+ processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
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+ model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
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+ model.to(DEVICE)
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+
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+ # Preprocessing the datasets.
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+ # We need to read the audio files as arrays
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+ def speech_file_to_array_fn(batch):
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+ with warnings.catch_warnings():
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+ warnings.simplefilter("ignore")
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+ speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
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+ batch["speech"] = speech_array
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+ batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper()
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+ return batch
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+
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+ test_dataset = test_dataset.map(speech_file_to_array_fn)
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+
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+ # Preprocessing the datasets.
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+ # We need to read the audio files as arrays
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+ def evaluate(batch):
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+ inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
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+
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+ with torch.no_grad():
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+ logits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits
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+
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+ pred_ids = torch.argmax(logits, dim=-1)
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+ batch["pred_strings"] = processor.batch_decode(pred_ids)
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+ return batch
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+
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+ result = test_dataset.map(evaluate, batched=True, batch_size=8)
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+
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+ predictions = [x.upper() for x in result["pred_strings"]]
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+ references = [x.upper() for x in result["sentence"]]
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+
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+ print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
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+ print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
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+ ```
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+
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+ **Test Result**:
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+
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+ My model may report better scores than others because of some specificity of my evaluation script, so I ran the same evaluation script on other models (on 2020-04-21) to make a fairer comparison.
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+
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+ | Model | WER | CER |
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+ | ------------- | ------------- | ------------- |
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+ | jonatasgrosman/wav2vec2-large-xlsr-53-arabic | **40.52%** | **18.37%** |
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+ | bakrianoo/sinai-voice-ar-stt | 45.30% | 21.84% |
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+ | othrif/wav2vec2-large-xlsr-arabic | 45.93% | 20.51% |
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+ | kmfoda/wav2vec2-large-xlsr-arabic | 54.14% | 26.07% |
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+ | anas/wav2vec2-large-xlsr-arabic | 62.02% | 27.09% |
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+ | elgeish/wav2vec2-large-xlsr-53-arabic | 100.00% | 100.56% |
config.json ADDED
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+ {
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+ "_name_or_path": "facebook/wav2vec2-large-xlsr-53",
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+ "activation_dropout": 0.05,
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+ "apply_spec_augment": true,
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+ "architectures": [
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+ "Wav2Vec2ForCTC"
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+ ],
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+ "attention_dropout": 0.1,
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+ "bos_token_id": 1,
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+ "conv_bias": true,
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+ "conv_dim": [
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+ 512,
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+ 512,
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+ 512,
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+ 512,
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+ 512,
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+ 512,
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+ 512
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+ ],
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+ "conv_kernel": [
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+ 10,
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+ 2
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+ ],
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+ "conv_stride": [
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+ 5,
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+ 2,
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+ 2,
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+ 2,
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+ 2
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+ ],
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+ "ctc_loss_reduction": "mean",
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+ "ctc_zero_infinity": true,
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+ "do_stable_layer_norm": true,
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+ "eos_token_id": 2,
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+ "feat_extract_activation": "gelu",
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+ "feat_extract_dropout": 0.0,
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+ "feat_extract_norm": "layer",
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+ "feat_proj_dropout": 0.05,
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+ "final_dropout": 0.0,
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+ "gradient_checkpointing": true,
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+ "hidden_act": "gelu",
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+ "hidden_dropout": 0.05,
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+ "hidden_size": 1024,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 4096,
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+ "layer_norm_eps": 1e-05,
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+ "layerdrop": 0.05,
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+ "mask_channel_length": 10,
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+ "mask_channel_min_space": 1,
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+ "mask_channel_other": 0.0,
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+ "mask_channel_prob": 0.0,
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+ "mask_channel_selection": "static",
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+ "mask_feature_length": 10,
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+ "mask_feature_prob": 0.0,
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+ "mask_time_length": 10,
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+ "mask_time_min_space": 1,
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+ "mask_time_other": 0.0,
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+ "mask_time_prob": 0.05,
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+ "mask_time_selection": "static",
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+ "model_type": "wav2vec2",
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+ "num_attention_heads": 16,
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+ "num_conv_pos_embedding_groups": 16,
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+ "num_conv_pos_embeddings": 128,
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+ "num_feat_extract_layers": 7,
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+ "num_hidden_layers": 24,
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+ "pad_token_id": 0,
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+ "transformers_version": "4.5.0.dev0",
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+ "vocab_size": 57
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+ }
preprocessor_config.json ADDED
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+ {
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+ "do_normalize": true,
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+ "feature_size": 1,
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+ "padding_side": "right",
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+ "padding_value": 0.0,
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+ "return_attention_mask": true,
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+ "sampling_rate": 16000
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+ }
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special_tokens_map.json ADDED
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+ {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
vocab.json ADDED
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+ {"<pad>": 0, "<s>": 1, "</s>": 2, "<unk>": 3, "|": 4, "ّ": 5, "ٌ": 6, "-": 7, "ر": 8, "ض": 9, "آ": 10, "ط": 11, "ٰ": 12, "ؤ": 13, "و": 14, "ق": 15, "ـ": 16, "ة": 17, "ِ": 18, "د": 19, "ذ": 20, "ز": 21, "ظ": 22, "ل": 23, "س": 24, "ْ": 25, "ُ": 26, "ف": 27, "ب": 28, "ش": 29, "ء": 30, "ۖ": 31, "ه": 32, "ت": 33, "ي": 34, "ج": 35, "ا": 36, "إ": 37, "ئ": 38, "أ": 39, "ك": 40, "ٍ": 41, "ً": 42, "ث": 43, "غ": 44, "خ": 45, "ک": 46, "ى": 47, "ص": 48, "َ": 49, "ی": 50, "ھ": 51, "م": 52, "ع": 53, "ن": 54, "؛": 55, "ح": 56}