Xabi Ezpeleta commited on
Commit
d206943
1 Parent(s): 027f7a3

Fine-tuned with CV17.0

Browse files
README.md CHANGED
@@ -1,43 +1,40 @@
1
  ---
2
- language:
3
- - eu
4
  license: apache-2.0
 
5
  tags:
6
- - whisper-event
7
  - generated_from_trainer
8
  datasets:
9
- - mozilla-foundation/common_voice_16_0
10
  metrics:
11
  - wer
12
  model-index:
13
- - name: Whisper Small Basque
14
  results:
15
  - task:
16
  name: Automatic Speech Recognition
17
  type: automatic-speech-recognition
18
  dataset:
19
- name: mozilla-foundation/common_voice_16_0 eu
20
- type: mozilla-foundation/common_voice_16_0
21
  config: eu
22
  split: test
23
  args: eu
24
  metrics:
25
  - name: Wer
26
  type: wer
27
- value: 12.012786552211754
28
  ---
29
 
30
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
31
  should probably proofread and complete it, then remove this comment. -->
32
 
33
- # Whisper Small Basque
34
 
35
- This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_16_0 eu dataset.
36
  It achieves the following results on the evaluation set:
37
- - Loss: 0.1996
38
- - Wer: 12.0128
39
-
40
- If you need to use this model with whisper.cpp, you can download the ggml file: [ggml-small-eu.bin](https://huggingface.co/xezpeleta/whisper-small-eu/resolve/main/ggml-small.eu.bin)
41
 
42
  ## Model description
43
 
@@ -60,7 +57,7 @@ The following hyperparameters were used during training:
60
  - train_batch_size: 32
61
  - eval_batch_size: 16
62
  - seed: 42
63
- - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
64
  - lr_scheduler_type: linear
65
  - lr_scheduler_warmup_steps: 500
66
  - training_steps: 5000
@@ -68,18 +65,18 @@ The following hyperparameters were used during training:
68
 
69
  ### Training results
70
 
71
- | Training Loss | Epoch | Step | Validation Loss | Wer |
72
- |:-------------:|:-----:|:----:|:---------------:|:-------:|
73
- | 0.2009 | 1.04 | 1000 | 0.2446 | 17.6881 |
74
- | 0.0759 | 2.09 | 2000 | 0.2102 | 14.2584 |
75
- | 0.0264 | 3.13 | 3000 | 0.2200 | 13.6898 |
76
- | 0.0633 | 5.02 | 4000 | 0.1955 | 12.5535 |
77
- | 0.0199 | 6.06 | 5000 | 0.1996 | 12.0128 |
78
 
79
 
80
  ### Framework versions
81
 
82
- - Transformers 4.26.0.dev0
83
- - Pytorch 1.13.1+cu117
84
- - Datasets 2.8.1.dev0
85
- - Tokenizers 0.13.2
 
1
  ---
2
+ library_name: transformers
 
3
  license: apache-2.0
4
+ base_model: openai/whisper-small
5
  tags:
 
6
  - generated_from_trainer
7
  datasets:
8
+ - common_voice_17_0
9
  metrics:
10
  - wer
11
  model-index:
12
+ - name: openai/whisper-small
13
  results:
14
  - task:
15
  name: Automatic Speech Recognition
16
  type: automatic-speech-recognition
17
  dataset:
18
+ name: common_voice_17_0
19
+ type: common_voice_17_0
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  config: eu
21
  split: test
22
  args: eu
23
  metrics:
24
  - name: Wer
25
  type: wer
26
+ value: 11.838103265051853
27
  ---
28
 
29
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
30
  should probably proofread and complete it, then remove this comment. -->
31
 
32
+ # openai/whisper-small
33
 
34
+ This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_17_0 dataset.
35
  It achieves the following results on the evaluation set:
36
+ - Loss: 0.1845
37
+ - Wer: 11.8381
 
 
38
 
39
  ## Model description
40
 
 
57
  - train_batch_size: 32
58
  - eval_batch_size: 16
59
  - seed: 42
60
+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
61
  - lr_scheduler_type: linear
62
  - lr_scheduler_warmup_steps: 500
63
  - training_steps: 5000
 
65
 
66
  ### Training results
67
 
68
+ | Training Loss | Epoch | Step | Validation Loss | Wer |
69
+ |:-------------:|:------:|:----:|:---------------:|:-------:|
70
+ | 0.2458 | 1.0296 | 1000 | 0.2480 | 18.0723 |
71
+ | 0.1099 | 2.0592 | 2000 | 0.1976 | 13.6355 |
72
+ | 0.0403 | 3.0888 | 3000 | 0.2002 | 12.7249 |
73
+ | 0.0211 | 4.1184 | 4000 | 0.2101 | 12.4327 |
74
+ | 0.0609 | 5.148 | 5000 | 0.1845 | 11.8381 |
75
 
76
 
77
  ### Framework versions
78
 
79
+ - Transformers 4.46.0.dev0
80
+ - Pytorch 2.4.1+cu121
81
+ - Datasets 3.0.2.dev0
82
+ - Tokenizers 0.20.0
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  "vocab_size": 51865
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  }
 
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preprocessor_config.json CHANGED
The diff for this file is too large to render. See raw diff
 
run.sh CHANGED
@@ -1,6 +1,6 @@
1
  python run_speech_recognition_seq2seq_streaming.py \
2
  --model_name_or_path="openai/whisper-small" \
3
- --dataset_name="mozilla-foundation/common_voice_16_0" \
4
  --dataset_config_name="eu" \
5
  --language="basque" \
6
  --train_split_name="train+validation" \
 
1
  python run_speech_recognition_seq2seq_streaming.py \
2
  --model_name_or_path="openai/whisper-small" \
3
+ --dataset_name="mozilla-foundation/common_voice_17_0" \
4
  --dataset_config_name="eu" \
5
  --language="basque" \
6
  --train_split_name="train+validation" \
run_speech_recognition_seq2seq_streaming.py CHANGED
@@ -274,7 +274,7 @@ def load_maybe_streaming_dataset(dataset_name, dataset_config_name, split="train
274
  if "+" in split:
275
  # load multiple splits separated by the `+` symbol with streaming mode
276
  dataset_splits = [
277
- load_dataset(dataset_name, dataset_config_name, split=split_name, streaming=streaming, **kwargs)
278
  for split_name in split.split("+")
279
  ]
280
  # interleave multiple splits to form one dataset
@@ -282,7 +282,7 @@ def load_maybe_streaming_dataset(dataset_name, dataset_config_name, split="train
282
  return interleaved_dataset
283
  else:
284
  # load a single split *with* streaming mode
285
- dataset = load_dataset(dataset_name, dataset_config_name, split=split, streaming=streaming, **kwargs)
286
  return dataset
287
 
288
 
@@ -357,7 +357,8 @@ def main():
357
  data_args.dataset_name,
358
  data_args.dataset_config_name,
359
  split=data_args.train_split_name,
360
- use_auth_token=True if model_args.use_auth_token else None,
 
361
  streaming=data_args.streaming,
362
  )
363
 
@@ -366,7 +367,7 @@ def main():
366
  data_args.dataset_name,
367
  data_args.dataset_config_name,
368
  split=data_args.eval_split_name,
369
- use_auth_token=True if model_args.use_auth_token else None,
370
  streaming=data_args.streaming,
371
  )
372
 
 
274
  if "+" in split:
275
  # load multiple splits separated by the `+` symbol with streaming mode
276
  dataset_splits = [
277
+ load_dataset(dataset_name, dataset_config_name, split=split_name, streaming=streaming, trust_remote_code=True, **kwargs)
278
  for split_name in split.split("+")
279
  ]
280
  # interleave multiple splits to form one dataset
 
282
  return interleaved_dataset
283
  else:
284
  # load a single split *with* streaming mode
285
+ dataset = load_dataset(dataset_name, dataset_config_name, split=split, streaming=streaming, trust_remote_code=True, **kwargs)
286
  return dataset
287
 
288
 
 
357
  data_args.dataset_name,
358
  data_args.dataset_config_name,
359
  split=data_args.train_split_name,
360
+ # xezpeleta
361
+ #use_auth_token=True if model_args.use_auth_token else None,
362
  streaming=data_args.streaming,
363
  )
364
 
 
367
  data_args.dataset_name,
368
  data_args.dataset_config_name,
369
  split=data_args.eval_split_name,
370
+ #use_auth_token=True if model_args.use_auth_token else None,
371
  streaming=data_args.streaming,
372
  )
373
 
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