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clu-ling/
whisper-large-v2-japanese-5k-steps

Name: whisper-large-v2-japanese-5k-steps
Availability: InStock

automatic-speech-recognition

generated_from_trainer

mozilla-foundation/common_voice_11_0

apache-2.0

endpoints_compatible

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whisper-large-v2-japanese-5k-steps

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

Loss: 0.4200
Wer: 0.7449

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: 50
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: 5000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0111	7.63	1000	0.3210	0.7888
0.0007	15.27	2000	0.3585	0.7478
0.0003	22.9	3000	0.3937	0.7432
0.0002	30.53	4000	0.4123	0.7443
0.0002	38.17	5000	0.4200	0.7449

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-japanese-5k-steps")
model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-large-v2-japanese-5k-steps").to(device)
forced_decoder_ids = processor.get_decoder_prompt_ids(language="ja", task="transcribe")

# load the dataset
commonvoice_eval = load_dataset("mozilla-foundation/common_voice_11_0", "ja", 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-japanese-5k-steps")
model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-large-v2-japanese-5k-steps")

# dataset
dataset = load_dataset("mozilla-foundation/common_voice_11_0", "ja", 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=7000, 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 = "ja", 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