Duplicate from bakrianoo/sinai-voice-ar-stt

Co-authored-by: Abu Bakr Soliman <bakrianoo@users.noreply.huggingface.co>

.gitattributes

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+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+- ar
+license: apache-2.0
+tags:
+- automatic-speech-recognition
+- hf-asr-leaderboard
+- robust-speech-event
+datasets:
+- mozilla-foundation/common_voice_8_0
+metrics:
+- wer
+- cer
+model-index:
+- name: Sinai Voice Arabic Speech Recognition Model
+  results:
+  - task:
+      type: automatic-speech-recognition
+      name: Speech Recognition
+    dataset:
+      type: mozilla-foundation/common_voice_8_0
+      name: Common Voice ar
+      args: ar
+    metrics:
+    - type: wer
+      value: 0.181
+      name: Test WER
+    - type: cer
+      value: 0.049
+      name: Test CER
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Robust Speech Event - Dev Data
+      type: speech-recognition-community-v2/dev_data
+      args: ar
+    metrics:
+    - name: Test WER
+      type: wer
+      value: 93.03
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Robust Speech Event - Test Data
+      type: speech-recognition-community-v2/eval_data
+      args: ar
+    metrics:
+    - name: Test WER
+      type: wer
+      value: 90.79
+widget:
+- example_title: Example 1
+  src: https://huggingface.co/bakrianoo/sinai-voice-ar-stt/raw/main/examples/common_voice_ar_19077324.mp3
+- example_title: Example 2
+  src: https://huggingface.co/bakrianoo/sinai-voice-ar-stt/raw/main/examples/common_voice_ar_19205138.mp3
+- example_title: Example 3
+  src: https://huggingface.co/bakrianoo/sinai-voice-ar-stt/raw/main/examples/common_voice_ar_19331711.mp3
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Sinai Voice Arabic Speech Recognition Model
+
+# نموذج **صوت سيناء** للتعرف على الأصوات العربية الفصحى و تحويلها إلى نصوص
+
+This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - AR dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2141
+- Wer: 0.1808
+
+It achieves the following results on the evaluation set:
+- eval_loss               =     0.2141
+- eval_samples            =      10388
+- eval_wer                =     0.181
+- eval_cer                =     0.049
+
+#### Evaluation Commands
+1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
+
+```bash
+python eval.py --model_id bakrianoo/sinai-voice-ar-stt --dataset mozilla-foundation/common_voice_8_0 --config ar --split test
+```
+
+
+### Inference Without LM
+
+```python
+from transformers import (Wav2Vec2Processor, Wav2Vec2ForCTC)
+import torchaudio
+import torch
+
+def speech_file_to_array_fn(voice_path, resampling_to=16000):
+    speech_array, sampling_rate = torchaudio.load(voice_path)
+    resampler = torchaudio.transforms.Resample(sampling_rate, resampling_to)
+    
+    return resampler(speech_array)[0].numpy(), sampling_rate
+
+# load the model
+cp = "bakrianoo/sinai-voice-ar-stt"
+processor = Wav2Vec2Processor.from_pretrained(cp)
+model = Wav2Vec2ForCTC.from_pretrained(cp)
+
+# recognize the text in a sample sound file
+sound_path = './my_voice.mp3'
+
+sample, sr = speech_file_to_array_fn(sound_path)
+inputs = processor([sample], sampling_rate=16_000, return_tensors="pt", padding=True)
+
+with torch.no_grad():
+    logits = model(inputs.input_values,).logits
+
+predicted_ids = torch.argmax(logits, dim=-1)
+
+print("Prediction:", processor.batch_decode(predicted_ids))
+```
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 32
+- eval_batch_size: 10
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- total_train_batch_size: 256
+- total_eval_batch_size: 80
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 1000
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Wer    |
+|:-------------:|:-----:|:-----:|:---------------:|:------:|
+| 1.354         | 0.64  | 1000  | 0.4109          | 0.4493 |
+| 0.5886        | 1.28  | 2000  | 0.2798          | 0.3099 |
+| 0.4977        | 1.92  | 3000  | 0.2387          | 0.2673 |
+| 0.4253        | 2.56  | 4000  | 0.2266          | 0.2523 |
+| 0.3942        | 3.2   | 5000  | 0.2171          | 0.2437 |
+| 0.3619        | 3.84  | 6000  | 0.2076          | 0.2253 |
+| 0.3245        | 4.48  | 7000  | 0.2088          | 0.2186 |
+| 0.308         | 5.12  | 8000  | 0.2086          | 0.2206 |
+| 0.2881        | 5.76  | 9000  | 0.2089          | 0.2105 |
+| 0.2557        | 6.4   | 10000 | 0.2015          | 0.2004 |
+| 0.248         | 7.04  | 11000 | 0.2044          | 0.1953 |
+| 0.2251        | 7.68  | 12000 | 0.2058          | 0.1932 |
+| 0.2052        | 8.32  | 13000 | 0.2117          | 0.1878 |
+| 0.1976        | 8.96  | 14000 | 0.2104          | 0.1825 |
+| 0.1845        | 9.6   | 15000 | 0.2156          | 0.1821 |
+
+
+### Framework versions
+
+- Transformers 4.16.2
+- Pytorch 1.10.2+cu113
+- Datasets 1.18.3
+- Tokenizers 0.11.0

added_tokens.json

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+{"<s>": 44, "</s>": 45}

all_results.json

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+{
+    "epoch": 10.0,
+    "eval_loss": 0.21412786841392517,
+    "eval_runtime": 70.9089,
+    "eval_samples": 10388,
+    "eval_samples_per_second": 146.498,
+    "eval_steps_per_second": 1.833,
+    "eval_wer": 0.18078979457836977,
+    "train_loss": 0.1316310991176183,
+    "train_runtime": 23113.6031,
+    "train_samples": 399991,
+    "train_samples_per_second": 173.054,
+    "train_steps_per_second": 0.676
+}

config.json

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+{
+  "_name_or_path": "facebook/wav2vec2-xls-r-300m",
+  "activation_dropout": 0.0,
+  "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": [
+    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": true,
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
+  "feat_extract_dropout": 0.0,
+  "feat_extract_norm": "layer",
+  "feat_proj_dropout": 0.0,
+  "feat_quantizer_dropout": 0.0,
+  "final_dropout": 0.0,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.0,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.0,
+  "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_prob": 0.05,
+  "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": 43,
+  "proj_codevector_dim": 768,
+  "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": "4.16.2",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 46,
+  "xvector_output_dim": 512
+}

eval.py

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+#!/usr/bin/env python3
+import argparse
+import re
+from typing import Dict
+
+import torch
+from datasets import Audio, Dataset, load_dataset, load_metric
+
+from transformers import AutoFeatureExtractor, pipeline
+
+
+def log_results(result: Dataset, args: Dict[str, str]):
+    """DO NOT CHANGE. This function computes and logs the result metrics."""
+
+    log_outputs = args.log_outputs
+    dataset_id = "_".join(args.dataset.split("/") + [args.config, args.split])
+
+    # load metric
+    wer = load_metric("wer")
+    cer = load_metric("cer")
+
+    # compute metrics
+    wer_result = wer.compute(references=result["target"], predictions=result["prediction"])
+    cer_result = cer.compute(references=result["target"], predictions=result["prediction"])
+
+    # print & log results
+    result_str = f"WER: {wer_result}\n" f"CER: {cer_result}"
+    print(result_str)
+
+    with open(f"{dataset_id}_eval_results.txt", "w") as f:
+        f.write(result_str)
+
+    # log all results in text file. Possibly interesting for analysis
+    if log_outputs is not None:
+        pred_file = f"log_{dataset_id}_predictions.txt"
+        target_file = f"log_{dataset_id}_targets.txt"
+
+        with open(pred_file, "w") as p, open(target_file, "w") as t:
+
+            # mapping function to write output
+            def write_to_file(batch, i):
+                p.write(f"{i}" + "\n")
+                p.write(batch["prediction"] + "\n")
+                t.write(f"{i}" + "\n")
+                t.write(batch["target"] + "\n")
+
+            result.map(write_to_file, with_indices=True)
+
+
+def normalize_text(text: str) -> str:
+    """DO ADAPT FOR YOUR USE CASE. this function normalizes the target text."""
+
+    chars_to_ignore_regex = '[zx.rﺃ“—`»NٍqAُ«☭ﻻْۛjQ,R?IDdٌOwemھa\'cۙMJ:”ًکWXZ؛;(ۘ…P)YCFٰۗsiۖklSng–fh\-Ep!ٓLVِۚBtyUTKHڨvbuGچَ؟]'
+
+    text = re.sub(chars_to_ignore_regex, "", text.lower())
+
+    # In addition, we can normalize the target text, e.g. removing new lines characters etc...
+    # note that order is important here!
+    token_sequences_to_ignore = ["\n\n", "\n", "   ", "  "]
+
+    for t in token_sequences_to_ignore:
+        text = " ".join(text.split(t))
+
+    return text
+
+
+def main(args):
+    # load dataset
+    dataset = load_dataset(args.dataset, args.config, split=args.split, use_auth_token=True)
+
+    # for testing: only process the first two examples as a test
+    # dataset = dataset.select(range(10))
+
+    # load processor
+    feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)
+    sampling_rate = feature_extractor.sampling_rate
+
+    # resample audio
+    dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))
+
+    # load eval pipeline
+    if args.device is None:
+        args.device = 0 if torch.cuda.is_available() else -1
+    asr = pipeline("automatic-speech-recognition", model=args.model_id, device=args.device)
+
+    # map function to decode audio
+    def map_to_pred(batch):
+        prediction = asr(
+            batch["audio"]["array"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s
+        )
+
+        batch["prediction"] = prediction["text"]
+        batch["target"] = normalize_text(batch["sentence"])
+        return batch
+
+    # run inference on all examples
+    result = dataset.map(map_to_pred, remove_columns=dataset.column_names, batch_size=5)
+
+    # compute and log_results
+    # do not change function below
+    log_results(result, args)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
+    )
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        required=True,
+        help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
+    )
+    parser.add_argument(
+        "--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'`  for Common Voice"
+    )
+    parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
+    parser.add_argument(
+        "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
+    )
+    parser.add_argument(
+        "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
+    )
+    parser.add_argument(
+        "--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
+    )
+    parser.add_argument(
+        "--device",
+        type=int,
+        default=None,
+        help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
+    )
+    args = parser.parse_args()
+
+    main(args)

eval_results.json

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+{
+    "epoch": 10.0,
+    "eval_loss": 0.21412786841392517,
+    "eval_runtime": 70.9089,
+    "eval_samples": 10388,
+    "eval_samples_per_second": 146.498,
+    "eval_steps_per_second": 1.833,
+    "eval_wer": 0.18078979457836977
+}

mozilla-foundation_common_voice_8_0_ar_test_eval_results.txt

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+WER: 0.18172268907563024
+CER: 0.04875182561226061

preprocessor_config.json

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+{
+  "do_normalize": true,
+  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 0,
+  "return_attention_mask": true,
+  "sampling_rate": 16000
+}

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:588e6341d51008b353be1115b1e1e34d86bad4f676b32277cba57e5f7cff526a
+size 1262112241

run_speech_recognition_ctc_bnb.py

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+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+
+""" Fine-tuning a 🤗 Transformers CTC model for automatic speech recognition"""
+
+import functools
+import json
+import logging
+import os
+import re
+import sys
+import warnings
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Union
+
+import datasets
+import numpy as np
+import torch
+from datasets import DatasetDict, load_dataset, load_metric
+
+import bitsandbytes as bnb
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoFeatureExtractor,
+    AutoModelForCTC,
+    AutoProcessor,
+    AutoTokenizer,
+    HfArgumentParser,
+    Trainer,
+    TrainingArguments,
+    Wav2Vec2Processor,
+    set_seed,
+)
+from transformers.trainer_pt_utils import get_parameter_names
+from transformers.trainer_utils import get_last_checkpoint, is_main_process
+from transformers.utils import check_min_version
+from transformers.utils.versions import require_version
+
+logger = logging.getLogger(__name__)
+
+
+def list_field(default=None, metadata=None):
+    return field(default_factory=lambda: default, metadata=metadata)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
+    )
+    tokenizer_name_or_path: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to pretrained tokenizer or tokenizer identifier from huggingface.co/models"},
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
+    )
+    freeze_feature_encoder: bool = field(
+        default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
+    )
+    attention_dropout: float = field(
+        default=0.0, metadata={"help": "The dropout ratio for the attention probabilities."}
+    )
+    activation_dropout: float = field(
+        default=0.0, metadata={"help": "The dropout ratio for activations inside the fully connected layer."}
+    )
+    feat_proj_dropout: float = field(default=0.0, metadata={"help": "The dropout ratio for the projected features."})
+    hidden_dropout: float = field(
+        default=0.0,
+        metadata={
+            "help": "The dropout probability for all fully connected layers in the embeddings, encoder, and pooler."
+        },
+    )
+    final_dropout: float = field(
+        default=0.0,
+        metadata={"help": "The dropout probability for the final projection layer."},
+    )
+    mask_time_prob: float = field(
+        default=0.05,
+        metadata={
+            "help": "Probability of each feature vector along the time axis to be chosen as the start of the vector"
+            "span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
+            "vectors will be masked along the time axis."
+        },
+    )
+    mask_time_length: int = field(
+        default=10,
+        metadata={"help": "Length of vector span to mask along the time axis."},
+    )
+    mask_feature_prob: float = field(
+        default=0.0,
+        metadata={
+            "help": "Probability of each feature vector along the feature axis to be chosen as the start of the vector"
+            "span to be masked. Approximately ``mask_feature_prob * sequence_length // mask_feature_length`` feature bins will be masked along the time axis."
+        },
+    )
+    mask_feature_length: int = field(
+        default=10,
+        metadata={"help": "Length of vector span to mask along the feature axis."},
+    )
+    layerdrop: float = field(default=0.0, metadata={"help": "The LayerDrop probability."})
+    ctc_loss_reduction: Optional[str] = field(
+        default="mean", metadata={"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."}
+    )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+
+    Using `HfArgumentParser` we can turn this class
+    into argparse arguments to be able to specify them on
+    the command line.
+    """
+
+    dataset_name: str = field(
+        metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: str = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    train_split_name: str = field(
+        default="train+validation",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    eval_split_name: str = field(
+        default="test",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
+    )
+    text_column_name: str = field(
+        default="text",
+        metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
+            "value if set."
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of validation examples to this "
+            "value if set."
+        },
+    )
+    chars_to_ignore: Optional[List[str]] = list_field(
+        default=['چ', 'y', 'ۗ', 'n', 'J', 'C', 'K', 'V', 'g', ';', 'M', '?', 'u', 'S', 'ٌ', 'h', 'ً', '“', 'ۛ', 'r', 'P', '–', 'ﻻ', 'W', 'p', "'", 'o', 'Z', 'ۘ', 'ٰ', 'D', 'B', 'U', 'ﺃ', 'E', 'a', '»', '(', 'X', 'f', 'َ', '\\', 'l', 'x', 'v', 'ۖ', 'w', '”', 'ٍ', 'F', 'j', 'H', '…', '`', 'ڨ', 'O', ',', 'q', 'A', 'ِ', 'ٓ', '!', '؛', 'I', 't', 'ک', 'z', 'k', 's', '؟', 'd', 'G', 'ۚ', 'T', '—', 'R', ')', '«', 'Q', '☭', 'L', 'N', '-', 'Y', 'e', '.', 'c', ':', 'i', 'm', 'ُ', 'ۙ', 'ْ', 'b', 'ھ'],
+        metadata={"help": "A list of characters to remove from the transcripts."},
+    )
+    eval_metrics: List[str] = list_field(
+        default=["wer"],
+        metadata={"help": "A list of metrics the model should be evaluated on. E.g. `'wer cer'`"},
+    )
+    max_duration_in_seconds: float = field(
+        default=20.0,
+        metadata={
+            "help": "Filter audio files that are longer than `max_duration_in_seconds` seconds to 'max_duration_in_seconds`"
+        },
+    )
+    min_duration_in_seconds: float = field(
+        default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
+    )
+    preprocessing_only: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether to only do data preprocessing and skip training. "
+            "This is especially useful when data preprocessing errors out in distributed training due to timeout. "
+            "In this case, one should run the preprocessing in a non-distributed setup with `preprocessing_only=True` "
+            "so that the cached datasets can consequently be loaded in distributed training"
+        },
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": "If :obj:`True`, will use the token generated when running"
+            ":obj:`transformers-cli login` as HTTP bearer authorization for remote files."
+        },
+    )
+    unk_token: str = field(
+        default="[UNK]",
+        metadata={"help": "The unk token for the tokenizer"},
+    )
+    pad_token: str = field(
+        default="[PAD]",
+        metadata={"help": "The padding token for the tokenizer"},
+    )
+    word_delimiter_token: str = field(
+        default="|",
+        metadata={"help": "The word delimiter token for the tokenizer"},
+    )
+    phoneme_language: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The target language that should be used be"
+            " passed to the tokenizer for tokenization. Note that"
+            " this is only relevant if the model classifies the"
+            " input audio to a sequence of phoneme sequences."
+        },
+    )
+
+
+@dataclass
+class DataCollatorCTCWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+    Args:
+        processor (:class:`~transformers.AutoProcessor`)
+            The processor used for proccessing the data.
+        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
+            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
+            among:
+            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+              sequence if provided).
+            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
+              maximum acceptable input length for the model if that argument is not provided.
+            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
+              different lengths).
+        max_length (:obj:`int`, `optional`):
+            Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).
+        max_length_labels (:obj:`int`, `optional`):
+            Maximum length of the ``labels`` returned list and optionally padding length (see above).
+        pad_to_multiple_of (:obj:`int`, `optional`):
+            If set will pad the sequence to a multiple of the provided value.
+            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
+            7.5 (Volta).
+    """
+
+    processor: AutoProcessor
+    padding: Union[bool, str] = "longest"
+    pad_to_multiple_of: Optional[int] = None
+    pad_to_multiple_of_labels: Optional[int] = None
+
+    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+        # split inputs and labels since they have to be of different lenghts and need
+        # different padding methods
+        input_features = [{"input_values": feature["input_values"]} for feature in features]
+        label_features = [{"input_ids": feature["labels"]} for feature in features]
+
+        batch = self.processor.pad(
+            input_features,
+            padding=self.padding,
+            pad_to_multiple_of=self.pad_to_multiple_of,
+            return_tensors="pt",
+        )
+
+        with self.processor.as_target_processor():
+            labels_batch = self.processor.pad(
+                label_features,
+                padding=self.padding,
+                pad_to_multiple_of=self.pad_to_multiple_of_labels,
+                return_tensors="pt",
+            )
+
+        # replace padding with -100 to ignore loss correctly
+        labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
+
+        batch["labels"] = labels
+
+        return batch
+
+
+def create_vocabulary_from_data(
+    datasets: DatasetDict,
+    word_delimiter_token: Optional[str] = None,
+    unk_token: Optional[str] = None,
+    pad_token: Optional[str] = None,
+):
+    # Given training and test labels create vocabulary
+    def extract_all_chars(batch):
+        all_text = " ".join(batch["target_text"])
+        vocab = list(set(all_text))
+        return {"vocab": [vocab], "all_text": [all_text]}
+
+    vocabs = datasets.map(
+        extract_all_chars,
+        batched=True,
+        batch_size=-1,
+        keep_in_memory=True,
+        remove_columns=datasets["train"].column_names,
+    )
+
+    # take union of all unique characters in each dataset
+    vocab_set = functools.reduce(
+        lambda vocab_1, vocab_2: set(vocab_1["vocab"][0]) | set(vocab_2["vocab"][0]), vocabs.values()
+    )
+
+    vocab_dict = {v: k for k, v in enumerate(sorted(list(vocab_set)))}
+
+    # replace white space with delimiter token
+    if word_delimiter_token is not None:
+        vocab_dict[word_delimiter_token] = vocab_dict[" "]
+        del vocab_dict[" "]
+
+    # add unk and pad token
+    if unk_token is not None:
+        vocab_dict[unk_token] = len(vocab_dict)
+
+    if pad_token is not None:
+        vocab_dict[pad_token] = len(vocab_dict)
+
+    return vocab_dict
+
+
+def main():
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Detecting last checkpoint.
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+        f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
+    )
+    # Set the verbosity to info of the Transformers logger (on main process only):
+    if is_main_process(training_args.local_rank):
+        transformers.utils.logging.set_verbosity_info()
+    logger.info("Training/evaluation parameters %s", training_args)
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # 1. First, let's load the dataset
+    raw_datasets = DatasetDict()
+
+    if training_args.do_train:
+        raw_datasets["train"] = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.train_split_name,
+            use_auth_token=data_args.use_auth_token,
+        )
+
+        if data_args.audio_column_name not in raw_datasets["train"].column_names:
+            raise ValueError(
+                f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. "
+                "Make sure to set `--audio_column_name` to the correct audio column - one of "
+                f"{', '.join(raw_datasets['train'].column_names)}."
+            )
+
+        if data_args.text_column_name not in raw_datasets["train"].column_names:
+            raise ValueError(
+                f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
+                "Make sure to set `--text_column_name` to the correct text column - one of "
+                f"{', '.join(raw_datasets['train'].column_names)}."
+            )
+
+        if data_args.max_train_samples is not None:
+            raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
+
+    if training_args.do_eval:
+        raw_datasets["eval"] = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            split=data_args.eval_split_name,
+            use_auth_token=data_args.use_auth_token,
+        )
+
+        if data_args.max_eval_samples is not None:
+            raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))
+
+    # 2. We remove some special characters from the datasets
+    # that make training complicated and do not help in transcribing the speech
+    # E.g. characters, such as `,` and `.` do not really have an acoustic characteristic
+    # that could be easily picked up by the model
+    chars_to_ignore_regex = (
+        f'[{"".join(data_args.chars_to_ignore)}]' if data_args.chars_to_ignore is not None else None
+    )
+    text_column_name = data_args.text_column_name
+
+    def remove_special_characters(batch):
+        if chars_to_ignore_regex is not None:
+            batch["target_text"] = re.sub(chars_to_ignore_regex, "", batch[text_column_name]).lower() + " "
+        else:
+            batch["target_text"] = batch[text_column_name].lower() + " "
+        return batch
+
+    with training_args.main_process_first(desc="dataset map special characters removal"):
+        raw_datasets = raw_datasets.map(
+            remove_special_characters,
+            remove_columns=[text_column_name],
+            desc="remove special characters from datasets",
+        )
+
+    # save special tokens for tokenizer
+    word_delimiter_token = data_args.word_delimiter_token
+    unk_token = data_args.unk_token
+    pad_token = data_args.pad_token
+
+    # 3. Next, let's load the config as we might need it to create
+    # the tokenizer
+    # load config
+    config = AutoConfig.from_pretrained(
+        model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token
+    )
+
+    # 4. Next, if no tokenizer file is defined,
+    # we create the vocabulary of the model by extracting all unique characters from
+    # the training and evaluation datasets
+    # We need to make sure that only first rank saves vocabulary
+    # make sure all processes wait until vocab is created
+    tokenizer_name_or_path = model_args.tokenizer_name_or_path
+    tokenizer_kwargs = {}
+    if tokenizer_name_or_path is None:
+        # save vocab in training output dir
+        tokenizer_name_or_path = training_args.output_dir
+
+        vocab_file = os.path.join(tokenizer_name_or_path, "vocab.json")
+
+        with training_args.main_process_first():
+            if training_args.overwrite_output_dir and os.path.isfile(vocab_file):
+                os.remove(vocab_file)
+
+        with training_args.main_process_first(desc="dataset map vocabulary creation"):
+            if not os.path.isfile(vocab_file):
+                os.makedirs(tokenizer_name_or_path, exist_ok=True)
+                vocab_dict = create_vocabulary_from_data(
+                    raw_datasets,
+                    word_delimiter_token=word_delimiter_token,
+                    unk_token=unk_token,
+                    pad_token=pad_token,
+                )
+
+                # save vocab dict to be loaded into tokenizer
+                with open(vocab_file, "w") as file:
+                    json.dump(vocab_dict, file)
+
+        # if tokenizer has just been created
+        # it is defined by `tokenizer_class` if present in config else by `model_type`
+        tokenizer_kwargs = {
+            "config": config if config.tokenizer_class is not None else None,
+            "tokenizer_type": config.model_type if config.tokenizer_class is None else None,
+            "unk_token": unk_token,
+            "pad_token": pad_token,
+            "word_delimiter_token": word_delimiter_token,
+        }
+
+    # 5. Now we can instantiate the feature extractor, tokenizer and model
+    # Note for distributed training, the .from_pretrained methods guarantee that only
+    # one local process can concurrently download model & vocab.
+
+    # load feature_extractor and tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(
+        tokenizer_name_or_path,
+        use_auth_token=data_args.use_auth_token,
+        **tokenizer_kwargs,
+    )
+    feature_extractor = AutoFeatureExtractor.from_pretrained(
+        model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token
+    )
+
+    # adapt config
+    config.update(
+        {
+            "feat_proj_dropout": model_args.feat_proj_dropout,
+            "attention_dropout": model_args.attention_dropout,
+            "hidden_dropout": model_args.hidden_dropout,
+            "final_dropout": model_args.final_dropout,
+            "mask_time_prob": model_args.mask_time_prob,
+            "mask_time_length": model_args.mask_time_length,
+            "mask_feature_prob": model_args.mask_feature_prob,
+            "mask_feature_length": model_args.mask_feature_length,
+            "gradient_checkpointing": training_args.gradient_checkpointing,
+            "layerdrop": model_args.layerdrop,
+            "ctc_loss_reduction": model_args.ctc_loss_reduction,
+            "pad_token_id": tokenizer.pad_token_id,
+            "vocab_size": len(tokenizer),
+            "activation_dropout": model_args.activation_dropout,
+        }
+    )
+
+    # create model
+    model = AutoModelForCTC.from_pretrained(
+        model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        config=config,
+        use_auth_token=data_args.use_auth_token,
+    )
+
+    # freeze encoder
+    if model_args.freeze_feature_encoder:
+        model.freeze_feature_encoder()
+
+    # 6. Now we preprocess the datasets including loading the audio, resampling and normalization
+    # Thankfully, `datasets` takes care of automatically loading and resampling the audio,
+    # so that we just need to set the correct target sampling rate and normalize the input
+    # via the `feature_extractor`
+
+    # make sure that dataset decodes audio with correct sampling rate
+    dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
+    if dataset_sampling_rate != feature_extractor.sampling_rate:
+        raw_datasets = raw_datasets.cast_column(
+            data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
+        )
+
+    # derive max & min input length for sample rate & max duration
+    max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
+    min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
+    audio_column_name = data_args.audio_column_name
+    num_workers = data_args.preprocessing_num_workers
+
+    # `phoneme_language` is only relevant if the model is fine-tuned on phoneme classification
+    phoneme_language = data_args.phoneme_language
+
+    # Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+    def prepare_dataset(batch):
+        # load audio
+        sample = batch[audio_column_name]
+
+        inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
+        batch["input_values"] = inputs.input_values[0]
+        batch["input_length"] = len(batch["input_values"])
+
+        # encode targets
+        additional_kwargs = {}
+        if phoneme_language is not None:
+            additional_kwargs["phonemizer_lang"] = phoneme_language
+
+        batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids
+        return batch
+
+    with training_args.main_process_first(desc="dataset map preprocessing"):
+        vectorized_datasets = raw_datasets.map(
+            prepare_dataset,
+            remove_columns=next(iter(raw_datasets.values())).column_names,
+            num_proc=num_workers,
+            desc="preprocess datasets",
+        )
+
+        def is_audio_in_length_range(length):
+            return length > min_input_length and length < max_input_length
+
+        # filter data that is shorter than min_input_length
+        vectorized_datasets = vectorized_datasets.filter(
+            is_audio_in_length_range,
+            num_proc=num_workers,
+            input_columns=["input_length"],
+        )
+
+    # 7. Next, we can prepare the training.
+    # Let's use word error rate (WER) as our evaluation metric,
+    # instantiate a data collator and the trainer
+
+    # Define evaluation metrics during training, *i.e.* word error rate, character error rate
+    eval_metrics = {metric: load_metric(metric) for metric in data_args.eval_metrics}
+
+    # for large datasets it is advised to run the preprocessing on a
+    # single machine first with ``args.preprocessing_only`` since there will mostly likely
+    # be a timeout when running the script in distributed mode.
+    # In a second step ``args.preprocessing_only`` can then be set to `False` to load the
+    # cached dataset
+    if data_args.preprocessing_only:
+        logger.info(f"Data preprocessing finished. Files cached at {vectorized_datasets.cache_files}")
+        return
+
+    def compute_metrics(pred):
+        pred_logits = pred.predictions
+        pred_ids = np.argmax(pred_logits, axis=-1)
+
+        pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
+
+        pred_str = tokenizer.batch_decode(pred_ids)
+        # we do not want to group tokens when computing the metrics
+        label_str = tokenizer.batch_decode(pred.label_ids, group_tokens=False)
+
+        metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()}
+
+        return metrics
+
+    # Now save everything to be able to create a single processor later
+    if is_main_process(training_args.local_rank):
+        # save feature extractor, tokenizer and config
+        feature_extractor.save_pretrained(training_args.output_dir)
+        tokenizer.save_pretrained(training_args.output_dir)
+        config.save_pretrained(training_args.output_dir)
+
+    try:
+        processor = AutoProcessor.from_pretrained(training_args.output_dir)
+    except (OSError, KeyError):
+        warnings.warn(
+            "Loading a processor from a feature extractor config that does not"
+            " include a `processor_class` attribute is deprecated and will be removed in v5. Please add the following "
+            " attribute to your `preprocessor_config.json` file to suppress this warning: "
+            " `'processor_class': 'Wav2Vec2Processor'`",
+            FutureWarning,
+        )
+        processor = Wav2Vec2Processor.from_pretrained(training_args.output_dir)
+
+    # Instantiate custom data collator
+    data_collator = DataCollatorCTCWithPadding(processor=processor)
+
+    decay_parameters = get_parameter_names(model, [torch.nn.LayerNorm])
+    decay_parameters = [name for name in decay_parameters if "bias" not in name]
+    optimizer_grouped_parameters = [
+        {
+            "params": [p for n, p in model.named_parameters() if n in decay_parameters],
+            "weight_decay": training_args.weight_decay,
+        },
+        {
+            "params": [p for n, p in model.named_parameters() if n not in decay_parameters],
+            "weight_decay": 0.0,
+        },
+    ]
+    optimizer = bnb.optim.Adam8bit(
+        params=optimizer_grouped_parameters,
+        lr=training_args.learning_rate,
+        betas=(training_args.adam_beta1, training_args.adam_beta2),
+        eps=training_args.adam_epsilon,
+    )
+
+    optimizers = (optimizer, None)
+
+    # Initialize Trainer
+    trainer = Trainer(
+        model=model,
+        data_collator=data_collator,
+        args=training_args,
+        compute_metrics=compute_metrics,
+        train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
+        eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
+        tokenizer=feature_extractor,
+        optimizers=optimizers,
+    )
+
+    # 8. Finally, we can start training
+
+    # Training
+    if training_args.do_train:
+
+        # use last checkpoint if exist
+        if last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        elif os.path.isdir(model_args.model_name_or_path):
+            checkpoint = model_args.model_name_or_path
+        else:
+            checkpoint = None
+
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        trainer.save_model()
+
+        metrics = train_result.metrics
+        max_train_samples = (
+            data_args.max_train_samples
+            if data_args.max_train_samples is not None
+            else len(vectorized_datasets["train"])
+        )
+        metrics["train_samples"] = min(max_train_samples, len(vectorized_datasets["train"]))
+
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+
+    # Evaluation
+    results = {}
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+        metrics = trainer.evaluate()
+        max_eval_samples = (
+            data_args.max_eval_samples if data_args.max_eval_samples is not None else len(vectorized_datasets["eval"])
+        )
+        metrics["eval_samples"] = min(max_eval_samples, len(vectorized_datasets["eval"]))
+
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # Write model card and (optionally) push to hub
+    config_name = data_args.dataset_config_name if data_args.dataset_config_name is not None else "na"
+    kwargs = {
+        "finetuned_from": model_args.model_name_or_path,
+        "tasks": "speech-recognition",
+        "tags": ["automatic-speech-recognition", data_args.dataset_name],
+        "dataset_args": f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split: {data_args.eval_split_name}",
+        "dataset": f"{data_args.dataset_name.upper()} - {config_name.upper()}",
+    }
+    if "common_voice" in data_args.dataset_name:
+        kwargs["language"] = config_name
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+    return results
+
+
+if __name__ == "__main__":
+    main()

special_tokens_map.json

@@ -0,0 +1 @@
+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "[UNK]", "pad_token": "[PAD]", "additional_special_tokens": [{"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}]}

tokenizer_config.json

@@ -0,0 +1 @@
+{"unk_token": "[UNK]", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "[PAD]", "do_lower_case": false, "word_delimiter_token": "|", "special_tokens_map_file": null, "tokenizer_file": null, "name_or_path": "/workspace/cv-corpus-8.0-2022-01-19/output", "tokenizer_class": "Wav2Vec2CTCTokenizer"}

train-experiments.py

@@ -0,0 +1,835 @@
+import pandas as pd
+from tqdm.auto import tqdm
+import random
+from p_tqdm import p_map
+from datasets import load_dataset, load_metric, Audio
+from datasets import load_from_disk, concatenate_datasets
+import torchaudio
+
+import functools
+import json
+import logging
+import os
+import re
+import sys
+import warnings
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Union
+from datasets import concatenate_datasets, load_dataset
+
+import datasets
+import numpy as np
+import torch
+from datasets import DatasetDict, load_dataset, load_metric, Dataset
+
+import bitsandbytes as bnb
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoFeatureExtractor,
+    AutoModelForCTC,
+    AutoProcessor,
+    AutoTokenizer,
+    HfArgumentParser,
+    Trainer,
+    TrainingArguments,
+    Wav2Vec2Processor,
+    set_seed,
+)
+from transformers.trainer_pt_utils import get_parameter_names
+from transformers.trainer_utils import get_last_checkpoint, is_main_process
+from transformers.utils import check_min_version
+from transformers.utils.versions import require_version
+
+logger = logging.getLogger(__name__)
+
+def list_field(default=None, metadata=None):
+    return field(default_factory=lambda: default, metadata=metadata)
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": ""}, default="hf-test/xls-r-dummy"
+    )
+    tokenizer_name_or_path: Optional[str] = field(
+        default=None,
+        metadata={"help": "hf-test/xls-r-dummy"},
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
+    )
+    freeze_feature_encoder: bool = field(
+        default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
+    )
+    attention_dropout: float = field(
+        default=0.0, metadata={"help": "The dropout ratio for the attention probabilities."}
+    )
+    activation_dropout: float = field(
+        default=0.0, metadata={"help": "The dropout ratio for activations inside the fully connected layer."}
+    )
+    feat_proj_dropout: float = field(default=0.0, metadata={"help": "The dropout ratio for the projected features."})
+    hidden_dropout: float = field(
+        default=0.0,
+        metadata={
+            "help": "The dropout probability for all fully connected layers in the embeddings, encoder, and pooler."
+        },
+    )
+    final_dropout: float = field(
+        default=0.0,
+        metadata={"help": "The dropout probability for the final projection layer."},
+    )
+    mask_time_prob: float = field(
+        default=0.05,
+        metadata={
+            "help": "Probability of each feature vector along the time axis to be chosen as the start of the vector"
+            "span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
+            "vectors will be masked along the time axis."
+        },
+    )
+    mask_time_length: int = field(
+        default=10,
+        metadata={"help": "Length of vector span to mask along the time axis."},
+    )
+    mask_feature_prob: float = field(
+        default=0.0,
+        metadata={
+            "help": "Probability of each feature vector along the feature axis to be chosen as the start of the vector"
+            "span to be masked. Approximately ``mask_feature_prob * sequence_length // mask_feature_length`` feature bins will be masked along the time axis."
+        },
+    )
+    mask_feature_length: int = field(
+        default=10,
+        metadata={"help": "Length of vector span to mask along the feature axis."},
+    )
+    layerdrop: float = field(default=0.0, metadata={"help": "The LayerDrop probability."})
+    ctc_loss_reduction: Optional[str] = field(
+        default="mean", metadata={"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."}
+    )
+
+
+# In[4]:
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+
+    Using `HfArgumentParser` we can turn this class
+    into argparse arguments to be able to specify them on
+    the command line.
+    """
+
+    dataset_name: str = field(
+        metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: str = field(
+        default="ab", metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    train_split_name: str = field(
+        default="train+validation",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    eval_split_name: str = field(
+        default="test",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    audio_column_name: str = field(
+        default="audio",
+        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
+    )
+    text_column_name: str = field(
+        default="text",
+        metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
+            "value if set."
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of validation examples to this "
+            "value if set."
+        },
+    )
+    chars_to_ignore: Optional[List[str]] = list_field(
+        default=None,
+        metadata={"help": "A list of characters to remove from the transcripts."},
+    )
+    eval_metrics: List[str] = list_field(
+        default=["wer"],
+        metadata={"help": "A list of metrics the model should be evaluated on. E.g. `'wer cer'`"},
+    )
+    max_duration_in_seconds: float = field(
+        default=20.0,
+        metadata={
+            "help": "Filter audio files that are longer than `max_duration_in_seconds` seconds to 'max_duration_in_seconds`"
+        },
+    )
+    min_duration_in_seconds: float = field(
+        default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
+    )
+    preprocessing_only: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether to only do data preprocessing and skip training. "
+            "This is especially useful when data preprocessing errors out in distributed training due to timeout. "
+            "In this case, one should run the preprocessing in a non-distributed setup with `preprocessing_only=True` "
+            "so that the cached datasets can consequently be loaded in distributed training"
+        },
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": "If :obj:`True`, will use the token generated when running"
+            ":obj:`transformers-cli login` as HTTP bearer authorization for remote files."
+        },
+    )
+    unk_token: str = field(
+        default="[UNK]",
+        metadata={"help": "The unk token for the tokenizer"},
+    )
+    pad_token: str = field(
+        default="[PAD]",
+        metadata={"help": "The padding token for the tokenizer"},
+    )
+    word_delimiter_token: str = field(
+        default="|",
+        metadata={"help": "The word delimiter token for the tokenizer"},
+    )
+    phoneme_language: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The target language that should be used be"
+            " passed to the tokenizer for tokenization. Note that"
+            " this is only relevant if the model classifies the"
+            " input audio to a sequence of phoneme sequences."
+        },
+    )
+
+
+# In[5]:
+
+
+@dataclass
+class DataCollatorCTCWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+    Args:
+        processor (:class:`~transformers.AutoProcessor`)
+            The processor used for proccessing the data.
+        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
+            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
+            among:
+            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+              sequence if provided).
+            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
+              maximum acceptable input length for the model if that argument is not provided.
+            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
+              different lengths).
+        max_length (:obj:`int`, `optional`):
+            Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).
+        max_length_labels (:obj:`int`, `optional`):
+            Maximum length of the ``labels`` returned list and optionally padding length (see above).
+        pad_to_multiple_of (:obj:`int`, `optional`):
+            If set will pad the sequence to a multiple of the provided value.
+            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
+            7.5 (Volta).
+    """
+
+    processor: AutoProcessor
+    padding: Union[bool, str] = "longest"
+    pad_to_multiple_of: Optional[int] = None
+    pad_to_multiple_of_labels: Optional[int] = None
+
+    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+        # split inputs and labels since they have to be of different lenghts and need
+        # different padding methods
+        input_features = [{"input_values": feature["input_values"]} for feature in features]
+        label_features = [{"input_ids": feature["labels"]} for feature in features]
+
+        batch = self.processor.pad(
+            input_features,
+            padding=self.padding,
+            pad_to_multiple_of=self.pad_to_multiple_of,
+            return_tensors="pt",
+        )
+
+        with self.processor.as_target_processor():
+            labels_batch = self.processor.pad(
+                label_features,
+                padding=self.padding,
+                pad_to_multiple_of=self.pad_to_multiple_of_labels,
+                return_tensors="pt",
+            )
+
+        # replace padding with -100 to ignore loss correctly
+        labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
+
+        batch["labels"] = labels
+
+        return batch
+
+# download the augmented Dataset from 
+# https://huggingface.co/datasets/bakrianoo/arabic-cv8-augmented
+
+base_path = "/workspace/cv-corpus-8.0-2022-01-19"
+
+# load augmented datasets
+train_ar_df = pd.read_csv(f"{base_path}/train.tsv", sep="\t")
+train_ar_df["audio"] = train_ar_df["path"]
+
+test_ar_df = pd.read_csv(f"{base_path}/test.tsv", sep="\t")
+test_ar_df["audio"] = test_ar_df["path"]
+
+train_ar_df = train_ar_df.sample(frac=1, random_state=101, ignore_index=True)
+
+raw_datasets = DatasetDict()
+
+# select Dataset range
+from_rows = 0
+to_rows = 500_000
+
+saved_vecs_path = f"{base_path}/saved_vec_dataset-{from_rows}-{to_rows}.ds"
+
+raw_datasets["train"] = Dataset.from_pandas(train_ar_df.iloc[from_rows:to_rows])
+raw_datasets["eval"] = Dataset.from_pandas(test_ar_df)
+
+# Audio casting
+raw_datasets["train"] = raw_datasets["train"].cast_column("audio", datasets.features.Audio(sampling_rate=16000))
+raw_datasets["eval"] = raw_datasets["eval"].cast_column("audio", datasets.features.Audio(sampling_rate=16000))
+
+
+parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
+
+model_args, data_args, training_args = parser.parse_dict({
+        "dataset_name": "mozilla-foundation/common_voice_8_0",
+        "model_name_or_path": "facebook/wav2vec2-xls-r-300m",
+        "dataset_config_name": "ar",
+        "overwrite_output_dir": False,
+
+        # "preprocessing_only": True,
+    
+        "output_dir": f"{base_path}/output",
+        "text_column_name": "sentence",
+        
+        "freeze_feature_encoder": True,
+        "gradient_checkpointing": True,
+        "group_by_length": False,
+        "push_to_hub": False,
+        "use_auth_token": True,
+        "do_train": True,
+        "do_eval": True,
+        
+        "per_device_train_batch_size":32,
+        "gradient_accumulation_steps":1,
+        "per_device_eval_batch_size":10,
+        
+        "metric_for_best_model":'wer',
+        "evaluation_strategy":"steps",
+        "eval_steps":1000,
+        "logging_strategy":"steps",
+        "logging_steps":500,
+        "save_strategy":"steps",
+        "save_steps":1000,
+        "num_train_epochs":10,
+        "fp16":True,
+        "learning_rate":2e-4,
+        "warmup_steps":1000,
+        "save_total_limit":8,
+        "chars_to_ignore": [':', 'T', '؟', 'ۖ', '…', 'x', 'چ', '?', '.', 'ْ', 'g', '☭', 'w', ';', ',', 'a', 'ۙ', 'e', '`', '“', '!', 'n', 's', '؛', 'ﺃ', 'r', 'ٓ', 'c', '-', 't', 'u', 'l', 'o', '»', 'ٰ', 'ۗ', 'h', 'ڨ', 'ۚ', 'S', '—', 'ٌ', 'm', '”', 'd', 'ۛ', 'H', 'ُ', 'ﻻ', 'y', 'M', 'ھ', 'ک', 'ٍ', 'A', 'ۘ', 'ِ', '–', 'i', 'f', "'", 'ً', '«', 'َ'] + ['\\', '(',')','-','b','c','d','e','g','i','k','p','q','r','u','v','x'],
+
+    })
+
+
+# See all possible arguments in src/transformers/training_args.py
+# or by passing the --help flag to this script.
+# We now keep distinct sets of args, for a cleaner separation of concerns.
+
+# Detecting last checkpoint.
+last_checkpoint = None
+if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+    last_checkpoint = get_last_checkpoint(training_args.output_dir)
+    if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+        raise ValueError(
+            f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+            "Use --overwrite_output_dir to overcome."
+        )
+    elif last_checkpoint is not None:
+        logger.info(
+            f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+            "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+        )
+
+
+# Setup logging
+logging.basicConfig(
+    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+    datefmt="%m/%d/%Y %H:%M:%S",
+    handlers=[logging.StreamHandler(sys.stdout)],
+)
+logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
+
+# Log on each process the small summary:
+logger.warning(
+    f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+    f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
+)
+
+# Set the verbosity to info of the Transformers logger (on main process only):
+if is_main_process(training_args.local_rank):
+    transformers.utils.logging.set_verbosity_info()
+logger.info("Training/evaluation parameters %s", training_args)
+
+
+# Set seed before initializing model.
+set_seed(training_args.seed)
+
+
+### Load Dataset
+
+
+chars_to_ignore_regex = (
+    f'[{"".join(data_args.chars_to_ignore)}]' if data_args.chars_to_ignore is not None else None
+)
+text_column_name = data_args.text_column_name
+
+
+def remove_special_characters(batch):
+    if chars_to_ignore_regex is not None:
+        batch["target_text"] = re.sub(chars_to_ignore_regex, "", batch[text_column_name]).lower() + " "
+    else:
+        batch["target_text"] = batch[text_column_name].lower() + " "
+    return batch
+
+with training_args.main_process_first(desc="dataset map special characters removal"):
+    
+    raw_datasets = raw_datasets.map(
+        remove_special_characters,
+        remove_columns=[text_column_name],
+        desc="remove special characters from datasets",
+    )
+
+
+data_args.word_delimiter_token
+
+
+# save special tokens for tokenizer
+word_delimiter_token = data_args.word_delimiter_token
+unk_token = data_args.unk_token
+pad_token = data_args.pad_token
+
+# 3. Next, let's load the config as we might need it to create
+# the tokenizer
+# load config
+config = AutoConfig.from_pretrained(
+    model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token
+)
+
+def create_vocabulary_from_data(
+    datasets: DatasetDict,
+    word_delimiter_token: Optional[str] = None,
+    unk_token: Optional[str] = None,
+    pad_token: Optional[str] = None,
+):
+    # Given training and test labels create vocabulary
+    def extract_all_chars(batch):
+        all_text = " ".join(batch["target_text"])
+        vocab = list(set(all_text))
+        return {"vocab": [vocab], "all_text": [all_text]}
+    
+    vocabs = datasets.map(
+        extract_all_chars,
+        batched=True,
+        batch_size=-1,
+        keep_in_memory=True,
+        remove_columns=datasets["train"].column_names,
+    )
+
+    # take union of all unique characters in each dataset
+    vocab_set = functools.reduce(
+        lambda vocab_1, vocab_2: set(vocab_1["vocab"][0]) | set(vocab_2["vocab"][0]), vocabs.values()
+    )
+
+
+    vocab_dict = {v: k for k, v in enumerate(sorted(list(vocab_set)))}
+
+    # replace white space with delimiter token
+    if word_delimiter_token is not None:
+        vocab_dict[word_delimiter_token] = vocab_dict[" "]
+        del vocab_dict[" "]
+
+    # add unk and pad token
+    if unk_token is not None:
+        vocab_dict[unk_token] = len(vocab_dict)
+
+    if pad_token is not None:
+        vocab_dict[pad_token] = len(vocab_dict)
+
+    return vocab_dict
+
+
+raw_datasets["train"] = raw_datasets["train"].remove_columns("file_id")
+
+
+# 4. Next, if no tokenizer file is defined,
+# we create the vocabulary of the model by extracting all unique characters from
+# the training and evaluation datasets
+# We need to make sure that only first rank saves vocabulary
+# make sure all processes wait until vocab is created
+tokenizer_name_or_path = model_args.tokenizer_name_or_path
+tokenizer_kwargs = {}
+if tokenizer_name_or_path is None:
+    # save vocab in training output dir
+    tokenizer_name_or_path = training_args.output_dir
+
+    vocab_file = os.path.join(tokenizer_name_or_path, "vocab.json")
+
+    with training_args.main_process_first():
+        if training_args.overwrite_output_dir and os.path.isfile(vocab_file):
+            os.remove(vocab_file)
+
+    with training_args.main_process_first(desc="dataset map vocabulary creation"):
+        if not os.path.isfile(vocab_file):
+            os.makedirs(tokenizer_name_or_path, exist_ok=True)
+            vocab_dict = create_vocabulary_from_data(
+                raw_datasets,
+                word_delimiter_token=word_delimiter_token,
+                unk_token=unk_token,
+                pad_token=pad_token,
+            )
+
+            # save vocab dict to be loaded into tokenizer
+            with open(vocab_file, "w") as file:
+                json.dump(vocab_dict, file)
+
+    # if tokenizer has just been created
+    # it is defined by `tokenizer_class` if present in config else by `model_type`
+    tokenizer_kwargs = {
+        "config": config if config.tokenizer_class is not None else None,
+        "tokenizer_type": config.model_type if config.tokenizer_class is None else None,
+        "unk_token": unk_token,
+        "pad_token": pad_token,
+        "word_delimiter_token": word_delimiter_token,
+    }
+
+
+# 5. Now we can instantiate the feature extractor, tokenizer and model
+# Note for distributed training, the .from_pretrained methods guarantee that only
+# one local process can concurrently download model & vocab.
+
+# load feature_extractor and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(
+    tokenizer_name_or_path,
+    use_auth_token=data_args.use_auth_token,
+    **tokenizer_kwargs,
+)
+feature_extractor = AutoFeatureExtractor.from_pretrained(
+    model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token
+)
+
+
+# adapt config
+config.update(
+    {
+        "feat_proj_dropout": model_args.feat_proj_dropout,
+        "attention_dropout": model_args.attention_dropout,
+        "hidden_dropout": model_args.hidden_dropout,
+        "final_dropout": model_args.final_dropout,
+        "mask_time_prob": model_args.mask_time_prob,
+        "mask_time_length": model_args.mask_time_length,
+        "mask_feature_prob": model_args.mask_feature_prob,
+        "mask_feature_length": model_args.mask_feature_length,
+        "gradient_checkpointing": training_args.gradient_checkpointing,
+        "layerdrop": model_args.layerdrop,
+        "ctc_loss_reduction": model_args.ctc_loss_reduction,
+        "pad_token_id": tokenizer.pad_token_id,
+        "vocab_size": len(tokenizer),
+        "activation_dropout": model_args.activation_dropout,
+    }
+)
+
+
+# create model
+model = AutoModelForCTC.from_pretrained(
+    model_args.model_name_or_path,
+    cache_dir=model_args.cache_dir,
+    config=config,
+    use_auth_token=data_args.use_auth_token,
+)
+
+# freeze encoder
+if model_args.freeze_feature_encoder:
+    model.freeze_feature_encoder()
+
+
+# 6. Now we preprocess the datasets including loading the audio, resampling and normalization
+# Thankfully, `datasets` takes care of automatically loading and resampling the audio,
+# so that we just need to set the correct target sampling rate and normalize the input
+# via the `feature_extractor`
+
+# make sure that dataset decodes audio with correct sampling rate
+dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
+if dataset_sampling_rate != feature_extractor.sampling_rate:
+    raw_datasets = raw_datasets.cast_column(
+        data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
+    )
+
+# derive max & min input length for sample rate & max duration
+max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
+min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
+
+audio_column_name = data_args.audio_column_name
+num_workers = data_args.preprocessing_num_workers
+
+# `phoneme_language` is only relevant if the model is fine-tuned on phoneme classification
+phoneme_language = data_args.phoneme_language
+
+
+# Preprocessing the datasets.
+# We need to read the audio files as arrays and tokenize the targets.
+def prepare_dataset(batch):
+    # load audio
+    sample = batch[audio_column_name]
+
+    inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
+    batch["input_values"] = inputs.input_values[0]
+    batch["input_length"] = len(batch["input_values"])
+
+    # encode targets
+    additional_kwargs = {}
+    if phoneme_language is not None:
+        additional_kwargs["phonemizer_lang"] = phoneme_language
+
+    batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids
+    return batch
+
+def vectorizing_record(audio_path, target_text):
+    batch = {}
+    
+    array, sampling_rate = torchaudio.load(audio_path, format="mp3")
+    
+    batch["input_values"] = array.mean(axis=0)
+    batch["input_length"] = len(array)
+
+    # encode targets
+    additional_kwargs = {}
+    if phoneme_language is not None:
+        additional_kwargs["phonemizer_lang"] = phoneme_language
+
+    batch["labels"] = tokenizer(target_text, **additional_kwargs).input_ids
+    return batch
+
+
+# In[ ]:
+
+print(f"========\n\n{num_workers}\n\n========")
+with training_args.main_process_first(desc="dataset map preprocessing"):
+    saved_vecs_path = f"{base_path}/saved_vec_dataset-{from_rows}-{to_rows}.ds"
+    if not os.path.exists(saved_vecs_path):
+
+        vectorized_datasets = raw_datasets.map(
+            prepare_dataset,
+            remove_columns=next(iter(raw_datasets.values())).column_names,
+            num_proc=num_workers,
+            desc="preprocess datasets",
+        )
+
+
+        def is_audio_in_length_range(length):
+            return length > min_input_length and length < max_input_length
+
+        # filter data that is shorter than min_input_length
+        vectorized_datasets = vectorized_datasets.filter(
+            is_audio_in_length_range,
+            num_proc=num_workers,
+            input_columns=["input_length"],
+        )
+        
+        # save to local disk
+        vectorized_datasets.save_to_disk(saved_vecs_path)
+    else:    
+        # read from disk
+        vectorized_datasets = load_from_disk(saved_vecs_path)
+
+print(vectorized_datasets)
+
+# 7. Next, we can prepare the training.
+# Let's use word error rate (WER) as our evaluation metric,
+# instantiate a data collator and the trainer
+
+# Define evaluation metrics during training, *i.e.* word error rate, character error rate
+eval_metrics = {metric: load_metric(metric) for metric in data_args.eval_metrics}
+
+vectorized_datasets["train"] = vectorized_datasets["train"].remove_columns("input_length")
+vectorized_datasets["eval"] = vectorized_datasets["eval"].remove_columns("input_length")
+
+# for large datasets it is advised to run the preprocessing on a
+# single machine first with ``args.preprocessing_only`` since there will mostly likely
+# be a timeout when running the script in distributed mode.
+# In a second step ``args.preprocessing_only`` can then be set to `False` to load the
+# cached dataset
+if data_args.preprocessing_only:
+    logger.info(f"Data preprocessing finished. Files cached at {vectorized_datasets.cache_files}")
+
+
+
+def compute_metrics(pred):
+    pred_logits = pred.predictions
+    pred_ids = np.argmax(pred_logits, axis=-1)
+
+    pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
+
+    pred_str = tokenizer.batch_decode(pred_ids)
+
+    # we do not want to group tokens when computing the metrics
+    label_str = tokenizer.batch_decode(pred.label_ids, group_tokens=False)
+
+    metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()}
+    return metrics
+
+# Now save everything to be able to create a single processor later
+if is_main_process(training_args.local_rank):
+    # save feature extractor, tokenizer and config
+    feature_extractor.save_pretrained(training_args.output_dir)
+    tokenizer.save_pretrained(training_args.output_dir)
+    config.save_pretrained(training_args.output_dir)
+
+try:
+    processor = AutoProcessor.from_pretrained(training_args.output_dir)
+except (OSError, KeyError):
+    warnings.warn(
+        "Loading a processor from a feature extractor config that does not"
+        " include a `processor_class` attribute is deprecated and will be removed in v5. Please add the following "
+        " attribute to your `preprocessor_config.json` file to suppress this warning: "
+        " `'processor_class': 'Wav2Vec2Processor'`",
+        FutureWarning,
+    )
+    processor = Wav2Vec2Processor.from_pretrained(training_args.output_dir)
+
+# Instantiate custom data collator
+data_collator = DataCollatorCTCWithPadding(processor=processor)
+
+
+decay_parameters = get_parameter_names(model, [torch.nn.LayerNorm])
+decay_parameters = [name for name in decay_parameters if "bias" not in name]
+
+optimizer_grouped_parameters = [
+    {
+        "params": [p for n, p in model.named_parameters() if n in decay_parameters],
+        "weight_decay": training_args.weight_decay,
+    },
+    {
+        "params": [p for n, p in model.named_parameters() if n not in decay_parameters],
+        "weight_decay": 0.0,
+    },
+]
+
+optimizer = bnb.optim.Adam8bit(
+    params=optimizer_grouped_parameters,
+    lr=training_args.learning_rate,
+    betas=(training_args.adam_beta1, training_args.adam_beta2),
+    eps=training_args.adam_epsilon,
+)
+
+optimizers = (optimizer, None)
+
+
+# Initialize Trainer
+trainer = Trainer(
+    model=model,
+    data_collator=data_collator,
+    args=training_args,
+    compute_metrics=compute_metrics,
+    train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
+    eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
+    tokenizer=feature_extractor,
+    optimizers=optimizers,
+)
+
+
+
+# 8. Finally, we can start training
+
+# Training
+if training_args.do_train and not data_args.preprocessing_only:
+
+    # use last checkpoint if exist
+    if last_checkpoint is not None:
+        checkpoint = last_checkpoint
+    elif os.path.isdir(model_args.model_name_or_path):
+        checkpoint = model_args.model_name_or_path
+    else:
+        checkpoint = None
+
+    train_result = trainer.train(resume_from_checkpoint=checkpoint)
+    trainer.save_model()
+
+    metrics = train_result.metrics
+    max_train_samples = (
+        data_args.max_train_samples
+        if data_args.max_train_samples is not None
+        else len(vectorized_datasets["train"])
+    )
+    metrics["train_samples"] = min(max_train_samples, len(vectorized_datasets["train"]))
+
+    trainer.log_metrics("train", metrics)
+    trainer.save_metrics("train", metrics)
+    trainer.save_state()
+
+
+# Evaluation
+results = {}
+if training_args.do_eval and not data_args.preprocessing_only:
+    logger.info("*** Evaluate ***")
+    metrics = trainer.evaluate()
+    max_eval_samples = (
+        data_args.max_eval_samples if data_args.max_eval_samples is not None else len(vectorized_datasets["eval"])
+    )
+    metrics["eval_samples"] = min(max_eval_samples, len(vectorized_datasets["eval"]))
+
+    trainer.log_metrics("eval", metrics)
+    trainer.save_metrics("eval", metrics)
+
+# Write model card and (optionally) push to hub
+config_name = data_args.dataset_config_name if data_args.dataset_config_name is not None else "na"
+kwargs = {
+    "finetuned_from": model_args.model_name_or_path,
+    "tasks": "speech-recognition",
+    "tags": ["automatic-speech-recognition", data_args.dataset_name],
+    "dataset_args": f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split: {data_args.eval_split_name}",
+    "dataset": f"{data_args.dataset_name.upper()} - {config_name.upper()}",
+}
+
+if not data_args.preprocessing_only:
+    if "common_voice" in data_args.dataset_name:
+        kwargs["language"] = config_name
+
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+    print(results)
+

train_results.json

@@ -0,0 +1,8 @@
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+    "train_runtime": 23113.6031,
+    "train_samples": 399991,
+    "train_samples_per_second": 173.054,
+    "train_steps_per_second": 0.676
+}

trainer_state.json

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training_args.bin

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