generated_from_trainer

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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:

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:

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