Training in progress, step 500

.ipynb_checkpoints/added_tokens-checkpoint.json

@@ -0,0 +1 @@
+{"<s>": 216, "</s>": 217}

.ipynb_checkpoints/config-checkpoint.json

@@ -0,0 +1,107 @@
+{
+  "_name_or_path": "facebook/wav2vec2-xls-r-300m",
+  "activation_dropout": 0.1,
+  "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": 0.33,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.75,
+  "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": 216,
+  "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": "4.17.0.dev0",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 218,
+  "xvector_output_dim": 512
+}

.ipynb_checkpoints/eval-checkpoint.py

@@ -0,0 +1,153 @@
+#!/usr/bin/env python3
+import argparse
+import re
+import unicodedata
+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)
+
+
+chars_to_remove_regex = r'[\,\?\.\!\-\_\;\:\"\“\%\‘\”\�\^]'
+
+def remove_accents(text):
+    nfkd_form = unicodedata.normalize('NFKD', text)
+    return u"".join([c for c in nfkd_form if not unicodedata.combining(c)])
+
+def remove_special_characters(text):
+    text = re.sub(chars_to_remove_regex, '', text).lower()
+    text = re.sub("ç", r'[cedille]', text)
+    text = re.sub("&", r'et', text)    
+    text = re.sub("%", r' pourcents', text)   
+    text = re.sub("([0-9]+)(,|.)([0-9+])", r'\1 virgule \3', text) 
+    text = re.sub("\$", r'dollar', text)
+    text = re.sub("\£", r'livre', text)
+    text = re.sub("\€", r'euro', text)
+    text = remove_accents(text)
+    text = re.sub(r"\[cedille\]", 'ç', text) + " "
+    return text
+    
+def normalize_text(text: str) -> str:
+    text = remove_special_characters(text)
+
+    # 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(20))
+
+    # 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"]# "".join(prediction["text"].split("<s>"))
+        batch["target"] = normalize_text(batch["sentence"])
+        return batch
+
+    # run inference on all examples
+    result = dataset.map(map_to_pred, remove_columns=dataset.column_names)
+
+    # 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)

.ipynb_checkpoints/run-checkpoint.sh

@@ -3,13 +3,14 @@ python run_speech_recognition_ctc.py \
 --dataset_name="mozilla-foundation/common_voice_8_0" \
 --model_name_or_path="facebook/wav2vec2-xls-r-300m" \
 --dataset_config_name="fr" \
+--tokenizer_name_or_path="./" \
 --output_dir="./" \
 --overwrite_output_dir \
---num_train_epochs="5" \
+--num_train_epochs="2" \
 --per_device_train_batch_size="64" \
 --per_device_eval_batch_size="64" \
 --gradient_accumulation_steps="1" \
---learning_rate="7e-5" \
+--learning_rate="1e-4" \
 --warmup_steps="1500" \
 --length_column_name="input_length" \
 --evaluation_strategy="steps" \
@@ -18,19 +19,19 @@ python run_speech_recognition_ctc.py \
 --eval_steps="500" \
 --logging_steps="100" \
 --layerdrop="0.0" \
---activation_dropout="0.1" \
---save_total_limit="3" \
+--activation_dropout="0.05" \
+--save_total_limit="2" \
 --freeze_feature_encoder \
 --feat_proj_dropout="0.0" \
 --mask_time_prob="0.75" \
 --mask_time_length="10" \
---mask_feature_prob="0.33" \
+--mask_feature_prob="0.4" \
 --mask_feature_length="10" \
 --gradient_checkpointing \
 --report_to="wandb" \
 --run_name="xls-r-300m-fr" \
---max_eval_samples="4000" \
---max_duration_in_seconds="5" \
+--max_eval_samples="6000" \
+--max_duration_in_seconds="9" \
 --use_auth_token \
 --fp16 \
 --group_by_length \

.ipynb_checkpoints/special_tokens_map-checkpoint.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}]}

.ipynb_checkpoints/tokenizer_config-checkpoint.json

@@ -0,0 +1 @@
+{"unk_token": "[UNK]", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "[PAD]", "do_lower_case": false, "word_delimiter_token": "|", "config": null, "tokenizer_type": "wav2vec2", "special_tokens_map_file": null, "tokenizer_file": null, "name_or_path": "./", "tokenizer_class": "Wav2Vec2CTCTokenizer"}

config.json

@@ -1,6 +1,6 @@
 {
   "_name_or_path": "facebook/wav2vec2-xls-r-300m",
-  "activation_dropout": 0.1,
+  "activation_dropout": 0.05,
   "adapter_kernel_size": 3,
   "adapter_stride": 2,
   "add_adapter": false,
@@ -61,7 +61,7 @@
   "layerdrop": 0.0,
   "mask_feature_length": 10,
   "mask_feature_min_masks": 0,
-  "mask_feature_prob": 0.33,
+  "mask_feature_prob": 0.4,
   "mask_time_length": 10,
   "mask_time_min_masks": 2,
   "mask_time_prob": 0.75,

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2286263c5fbbc1e83cd9d35130517d66808e769f2dfa0b9424795299c9f1570b
+oid sha256:72685a6be9d4cf07d2dca978c04109b7eaf6f1812b0c6e98ad4e1009fcb3ebb0
 size 1262817457

special_tokens_map.json

@@ -1 +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}]}
+{"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}]}

training_args.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:045ff4a8456a52acfb08e668118d28bcb89545214b55b6b4b0122ab5c2b1ef4e
+oid sha256:3515fcb3146dcedc28056bf8ff0c0b0a7c0ebdb32789529ea25826588fbd9491
 size 3055

wandb/debug-internal.log

@@ -1 +1 @@
-run-20220129_131141-h6nhqm30/logs/debug-internal.log
+run-20220129_215451-1vipdbow/logs/debug-internal.log

wandb/debug.log

@@ -1 +1 @@
-run-20220129_131141-h6nhqm30/logs/debug.log
+run-20220129_215451-1vipdbow/logs/debug.log

wandb/latest-run

@@ -1 +1 @@
-run-20220129_131141-h6nhqm30
+run-20220129_215451-1vipdbow

wandb/run-20220129_215451-1vipdbow/files/output.log

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+  1%|█▏                                                                                                                                                              | 99/13822 [05:17<6:46:15,  1.78s/it]
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+  4%|█████▊                                                                                                                                                         | 500/13822 [26:21<6:22:44,  1.72s/it]The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.
+***** Running Evaluation *****
+  Num examples = 5792
+  Batch size = 64
+
+
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+Configuration saved in ./checkpoint-500/config.json
+{'eval_loss': 3.878422975540161, 'eval_wer': 1.0, 'eval_runtime': 285.8223, 'eval_samples_per_second': 20.264, 'eval_steps_per_second': 0.318, 'epoch': 0.07}
+Model weights saved in ./checkpoint-500/pytorch_model.bin
+Configuration saved in ./checkpoint-500/preprocessor_config.json

wandb/run-20220129_215451-1vipdbow/files/requirements.txt

@@ -0,0 +1,180 @@
+aiohttp==3.8.1
+aiosignal==1.2.0
+analytics-python==1.4.0
+anyio==3.5.0
+appdirs==1.4.4
+argon2-cffi-bindings==21.2.0
+argon2-cffi==21.3.0
+asgiref==3.5.0
+asttokens==2.0.5
+async-timeout==4.0.2
+attrs==21.4.0
+audioread==2.1.9
+backcall==0.2.0
+backoff==1.10.0
+bcrypt==3.2.0
+beautifulsoup4==4.9.3
+black==21.12b0
+bleach==4.1.0
+brotlipy==0.7.0
+certifi==2020.12.5
+cffi==1.14.3
+chardet==3.0.4
+charset-normalizer==2.0.10
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+conda-build==3.21.4
+conda-package-handling==1.7.2
+conda==4.9.2
+configparser==5.2.0
+cryptography==3.2.1
+cycler==0.11.0
+datasets==1.18.2.dev0
+debugpy==1.5.1
+decorator==4.4.2
+defusedxml==0.7.1
+dill==0.3.4
+dnspython==2.1.0
+docker-pycreds==0.4.0
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+gitpython==3.1.26
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wandb/run-20220129_215451-1vipdbow/files/wandb-metadata.json

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+    "python": "3.8.8",
+    "heartbeatAt": "2022-01-29T21:54:53.025463",
+    "startedAt": "2022-01-29T21:54:51.704109",
+    "docker": null,
+    "gpu": "Tesla V100S-PCIE-32GB",
+    "gpu_count": 1,
+    "cpu_count": 60,
+    "cuda": null,
+    "args": [
+        "--dataset_name=mozilla-foundation/common_voice_8_0",
+        "--model_name_or_path=facebook/wav2vec2-xls-r-300m",
+        "--dataset_config_name=fr",
+        "--tokenizer_name_or_path=./",
+        "--output_dir=./",
+        "--overwrite_output_dir",
+        "--num_train_epochs=2",
+        "--per_device_train_batch_size=64",
+        "--per_device_eval_batch_size=64",
+        "--gradient_accumulation_steps=1",
+        "--learning_rate=1e-4",
+        "--warmup_steps=1500",
+        "--length_column_name=input_length",
+        "--evaluation_strategy=steps",
+        "--text_column_name=sentence",
+        "--save_steps=500",
+        "--eval_steps=500",
+        "--logging_steps=100",
+        "--layerdrop=0.0",
+        "--activation_dropout=0.05",
+        "--save_total_limit=2",
+        "--freeze_feature_encoder",
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+        "--mask_feature_length=10",
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+        "--report_to=wandb",
+        "--run_name=xls-r-300m-fr",
+        "--max_eval_samples=6000",
+        "--max_duration_in_seconds=9",
+        "--use_auth_token",
+        "--fp16",
+        "--group_by_length",
+        "--preprocessing_num_workers=64",
+        "--do_train",
+        "--do_eval",
+        "--load_best_model_at_end",
+        "--push_to_hub"
+    ],
+    "state": "running",
+    "program": "run_speech_recognition_ctc.py",
+    "codePath": "run_speech_recognition_ctc.py",
+    "git": {
+        "remote": "https://huggingface.co/AlexN/xls-r-300m-fr",
+        "commit": "c58dbaf4476093ff9758e2d11bbf63f828a0ecc3"
+    },
+    "email": "alexandrenap@hotmail.fr",
+    "root": "/workspace/xls-r-300m-fr",
+    "host": "job-1abccd0a-3293-4ffe-8274-9e8f841f653f",
+    "username": "ovh",
+    "executable": "/opt/conda/bin/python"
+}

wandb/run-20220129_215451-1vipdbow/logs/debug.log

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