whisper-large-v2-spanish
This model is a fine-tuned version of openai/whisper-large-v2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1466
- Wer: 0.0855
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
---|---|---|---|---|
0.1908 | 0.03 | 1000 | 0.2235 | 0.1154 |
0.1888 | 0.07 | 2000 | 0.2132 | 0.1131 |
0.167 | 0.1 | 3000 | 0.2115 | 0.1133 |
0.1752 | 0.14 | 4000 | 0.2081 | 0.1146 |
0.1656 | 0.17 | 5000 | 0.2002 | 0.1073 |
0.1535 | 0.21 | 6000 | 0.1971 | 0.1086 |
0.1854 | 0.24 | 7000 | 0.1927 | 0.1048 |
0.1722 | 0.28 | 8000 | 0.1889 | 0.1043 |
0.166 | 0.31 | 9000 | 0.1850 | 0.1022 |
0.1277 | 0.35 | 10000 | 0.1820 | 0.1032 |
0.1457 | 0.38 | 11000 | 0.1777 | 0.0998 |
0.169 | 0.42 | 12000 | 0.1771 | 0.0982 |
0.1612 | 0.45 | 13000 | 0.1724 | 0.0976 |
0.1616 | 0.49 | 14000 | 0.1693 | 0.0956 |
0.1556 | 0.52 | 15000 | 0.1671 | 0.0942 |
0.1448 | 0.56 | 16000 | 0.1646 | 0.0930 |
0.117 | 0.59 | 17000 | 0.1613 | 0.0914 |
0.1441 | 0.62 | 18000 | 0.1596 | 0.0899 |
0.148 | 0.66 | 19000 | 0.1571 | 0.0895 |
0.1255 | 0.69 | 20000 | 0.1547 | 0.0874 |
0.1479 | 0.73 | 21000 | 0.1525 | 0.0885 |
0.1304 | 0.76 | 22000 | 0.1503 | 0.0861 |
0.1111 | 0.8 | 23000 | 0.1486 | 0.0867 |
0.1337 | 0.83 | 24000 | 0.1472 | 0.0854 |
0.1289 | 0.87 | 25000 | 0.1466 | 0.0855 |
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-large-v2-spanish")
model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-large-v2-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-large-v2-spanish")
model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-large-v2-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 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
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