---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-spanish
  results: []
---

<!-- 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. -->

# whisper-small-sp

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the `commonvoice dataset v11` dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4485
- Wer: 20.6842

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- 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: 25000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 2.2671        | 0.13  | 1000  | 2.2108          | 76.2667 |
| 1.4465        | 0.26  | 2000  | 1.6057          | 67.8753 |
| 1.0997        | 0.39  | 3000  | 1.1928          | 54.2433 |
| 0.9389        | 0.52  | 4000  | 1.0020          | 47.8307 |
| 0.7881        | 0.65  | 5000  | 0.8933          | 46.0046 |
| 0.7596        | 0.78  | 6000  | 0.7721          | 38.5595 |
| 0.5678        | 0.91  | 7000  | 0.6903          | 36.2897 |
| 0.4412        | 1.04  | 8000  | 0.6476          | 32.7473 |
| 0.4239        | 1.17  | 9000  | 0.5973          | 30.8142 |
| 0.3935        | 1.3   | 10000 | 0.5444          | 29.0208 |
| 0.3307        | 1.43  | 11000 | 0.5024          | 27.0434 |
| 0.2937        | 1.56  | 12000 | 0.4608          | 24.7318 |
| 0.2471        | 1.69  | 13000 | 0.4259          | 22.8940 |
| 0.2357        | 1.82  | 14000 | 0.3936          | 21.6018 |
| 0.2292        | 1.95  | 15000 | 0.3776          | 20.8004 |
| 0.1493        | 2.08  | 16000 | 0.4599          | 24.0491 |
| 0.1708        | 2.21  | 17000 | 0.4370          | 23.3443 |
| 0.1385        | 2.34  | 18000 | 0.4277          | 22.3171 |
| 0.1288        | 2.47  | 19000 | 0.4050          | 21.0118 |
| 0.1627        | 2.6   | 20000 | 0.4507          | 23.4004 |
| 0.1675        | 2.73  | 21000 | 0.4346          | 22.8261 |
| 0.159         | 2.86  | 22000 | 0.4179          | 22.2949 |
| 0.1458        | 2.99  | 23000 | 0.3978          | 21.0810 |
| 0.0487        | 3.12  | 24000 | 0.4456          | 20.8617 |
| 0.0401        | 3.25  | 25000 | 0.4485          | 20.6842 |

### 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-small-spanish")
model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-small-spanish").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-small-spanish")
model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-small-spanish")

# 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 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2