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# whisper-large-v2-spanish-english

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Spanish CommonVoice dataset (v11).
It achieves the following results on the evaluation set:
- Loss: 0.2297
- Wer: 0.1282

## Model description

This model is finetuned for 5000 steps for research purposes which means that the transcriptions might not be that satisfactory for users.

## Training and evaluation data

- Training Data: CommonVoice (v11) train split
- Validation Data: CommonVoice (v11) Validation split
- Test Data: CommonVoice (v11) Test split

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1585        | 0.14  | 1000 | 0.3235          | 0.2571 |
| 0.1441        | 0.28  | 2000 | 0.2746          | 0.1976 |
| 0.1282        | 0.42  | 3000 | 0.2517          | 0.1506 |
| 0.1361        | 0.56  | 4000 | 0.2372          | 0.1330 |
| 0.1211        | 0.69  | 5000 | 0.2297          | 0.1282 |

### Transcription:

```python
from datasets import load_dataset, Audio
import torch
from transformers import WhisperProcessor, WhisperForConditionalGeneration

# device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# load the model
processor = WhisperProcessor.from_pretrained("clu-ling/whisper-large-v2-spanish-5k-steps")
model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-large-v2-spanish-5k-steps").to(device)
forced_decoder_ids = processor.get_decoder_prompt_ids(language="es", task="transcribe")

# load the dataset
commonvoice_eval = load_dataset("mozilla-foundation/common_voice_11_0", "es", split="validation", streaming=True)
commonvoice_eval = commonvoice_eval.cast_column("audio", Audio(sampling_rate=16000))
sample = next(iter(commonvoice_eval))["audio"]

# features and generate token ids
input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features
predicted_ids = model.generate(input_features.to(device), forced_decoder_ids=forced_decoder_ids)

# decode
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)

print(transcription)

```

### Evaluation:

Evaluates this model on `mozilla-foundation/common_voice_11_0` test split.

```python
from transformers.models.whisper.english_normalizer import BasicTextNormalizer
from datasets import load_dataset, Audio
import evaluate
import torch
import re
from transformers import WhisperProcessor, WhisperForConditionalGeneration

# device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# metric
wer_metric = evaluate.load("wer")

# model
processor = WhisperProcessor.from_pretrained("clu-ling/whisper-large-v2-spanish-5k-steps")
model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-large-v2-spanish-5k-steps")

# dataset
dataset = load_dataset("mozilla-foundation/common_voice_11_0", "es", split="test", )#cache_dir=args.cache_dir
dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))

#for debuggings: it gets some examples
#dataset = dataset.shard(num_shards=10000, index=0)
#print(dataset)
   
def normalize(batch):
  batch["gold_text"] = whisper_norm(batch['sentence'])
  return batch

def map_wer(batch):
  model.to(device)
  forced_decoder_ids = processor.get_decoder_prompt_ids(language = "es", task = "transcribe")
  inputs = processor(batch["audio"]["array"], sampling_rate=batch["audio"]["sampling_rate"], return_tensors="pt").input_features
  with torch.no_grad():
    generated_ids = model.generate(inputs=inputs.to(device), forced_decoder_ids=forced_decoder_ids)
    transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
  batch["predicted_text"] = whisper_norm(transcription)
  return batch

# process GOLD text
processed_dataset = dataset.map(normalize)
# get predictions
predicted = processed_dataset.map(map_wer)

# word error rate
wer = wer_metric.compute(references=predicted['gold_text'], predictions=predicted['predicted_text'])
wer = round(100 * wer, 2)
print("WER:", wer)


```

### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.1
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
{"language": ["es"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_11_0"], "metrics": ["wer"], "model-index": [{"name": "whisper-large-v2-spanish-english", "results": []}]}
transformers pytorch tensorboard whisper automatic-speech-recognition generated_from_trainer es dataset:mozilla-foundation/common_voice_11_0 license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #es #dataset-mozilla-foundation/common_voice_11_0 #license-apache-2.0 #endpoints_compatible #has_space #region-us 

whisper-large-v2-spanish-english
================================


This model is a fine-tuned version of openai/whisper-large-v2 on the Spanish CommonVoice dataset (v11).
It achieves the following results on the evaluation set:


* Loss: 0.2297
* Wer: 0.1282


Model description
-----------------


This model is finetuned for 5000 steps for research purposes which means that the transcriptions might not be that satisfactory for users.


Training and evaluation data
----------------------------


* Training Data: CommonVoice (v11) train split
* Validation Data: CommonVoice (v11) Validation split
* Test Data: CommonVoice (v11) Test split


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 5000
* mixed\_precision\_training: Native AMP


### Training results



### Transcription:


### Evaluation:


Evaluates this model on 'mozilla-foundation/common\_voice\_11\_0' test split.


### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.1
* Datasets 2.8.1.dev0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 5000
* mixed\_precision\_training: Native AMP ### Training results ### Transcription: ### Evaluation:


Evaluates this model on 'mozilla-foundation/common\_voice\_11\_0' test split. ### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.1
* Datasets 2.8.1.dev0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #es #dataset-mozilla-foundation/common_voice_11_0 #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 5000
* mixed\_precision\_training: Native AMP ### Training results ### Transcription: ### Evaluation:


Evaluates this model on 'mozilla-foundation/common\_voice\_11\_0' test split. ### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.1
* Datasets 2.8.1.dev0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #es #dataset-mozilla-foundation/common_voice_11_0 #license-apache-2.0 #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 5000
* mixed\_precision\_training: Native AMP### Training results### Transcription:### Evaluation:


Evaluates this model on 'mozilla-foundation/common\_voice\_11\_0' test split.### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.1
* Datasets 2.8.1.dev0
* Tokenizers 0.13.2