clu-ling/whisper-small-spanish

whisper-small-sp

This model is a fine-tuned version of 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:

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.

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