Whisper-Tiny Dutch - Full Synthetic Data (Unfiltered)

This model is a fine-tuned version of openai/whisper-tiny for Dutch automatic speech recognition (ASR). It was trained on Common Voice 17.0 Dutch combined with all synthetic speech data without quality filtering, representing the maximum data augmentation approach.

Introduction

Purpose

This model uses all available synthetic data without WAVe quality filtering to evaluate the impact of maximum data augmentation. While it achieves the best in-domain Test WER (24.93%), it requires significantly more training steps than filtered approaches, demonstrating the quality-vs-quantity tradeoff in synthetic data augmentation.

How the Data Was Created

The training data combines real speech from Common Voice 17.0 with the complete synthetic dataset:

1. Transcript Generation: We used GPT-4o-mini to generate Dutch transcripts that match the word count distribution observed in Common Voice, ensuring realistic utterance lengths and diverse linguistic content.

2. Speech Synthesis: Each transcript was converted to audio using OpenAI's TTS-1 model with 9 different voice variants (alloy, ash, coral, echo, fable, nova, onyx, sage, shimmer), producing 34,898 synthetic samples.

3. No Quality Filtering: Unlike other models in this series, no WAVe filtering was applied. All 34,898 synthetic samples were used, including those with potential synthesis defects.

How the Model Was Created

The model was fine-tuned from openai/whisper-tiny using the Hugging Face Transformers library:

1. Mixed Training: Combined 34,952 real speech samples from Common Voice 17.0 Dutch with all 34,898 synthetic samples (69,850 total).

2. Optimization: Trained for 5 epochs with a learning rate of 5e-5, global batch size of 256, and BF16 precision on an NVIDIA H200 GPU.

3. Checkpoint Selection: The best checkpoint was selected based on validation loss, occurring at step 800 with a validation loss of 0.3271.

This approach achieves the best in-domain performance (24.93% Test WER) but requires 60% more training steps than the high-quality filtered approach.

Model Details

Evaluation Results

This Model (whisper-tiny-cv-fully-synthetic-nl)

Comparison with Other Training Configurations

Key Performance Highlights

• Best in-domain Test WER (24.93%) among all Whisper-Tiny Dutch configurations
• Lowest validation loss (0.3207) indicating strong model fit
• 4.1% relative improvement on Common Voice test set vs baseline (24.93% vs 26.00%)
• 3.9% relative improvement on MLS benchmark vs baseline (43.12% vs 44.85%)
• Tradeoff: Requires 1,365 steps vs 890 for high-quality filtered (53% more compute)

Training Data

Dataset Composition

Synthetic Data Generation Pipeline

The synthetic dataset (yuriyvnv/synthetic_transcript_nl) was generated using:

1. Transcript Generation: GPT-4o-mini, matching Common Voice word count distribution
2. Speech Synthesis: OpenAI TTS-1 model with 9 voice variants (alloy, ash, coral, echo, fable, nova, onyx, sage, shimmer)
3. No Filtering: All samples used regardless of quality

Quality Distribution (For Reference)

While this model uses all data, WAVe quality assessment shows the distribution:

Note: 13.5% of the synthetic data (4,716 samples) would be filtered out by WAVe, but is included in this model's training.

Training Procedure

Hyperparameters

Training Infrastructure

• GPU: NVIDIA H200 (140GB VRAM)
• Operating System: Ubuntu 22.04
• Framework: Hugging Face Transformers

Training Curve

Step  100: val_loss = 0.5266
Step  250: val_loss = 0.4054
Step  500: val_loss = 0.3454
Step  800: val_loss = 0.3271 ← Best checkpoint
Step 1000: val_loss = 0.3311
Step 1200: val_loss = 0.3331
Step 1350: val_loss = 0.3331

Usage

Transcription Pipeline

from transformers import pipeline

transcriber = pipeline(
    "automatic-speech-recognition",
    model="yuriyvnv/whisper-tiny-cv-fully-synthetic-nl",
    device="cuda"
)

result = transcriber("path/to/dutch_audio.wav")
print(result["text"])

Direct Model Usage

from transformers import WhisperProcessor, WhisperForConditionalGeneration
import librosa

processor = WhisperProcessor.from_pretrained("yuriyvnv/whisper-tiny-cv-fully-synthetic-nl")
model = WhisperForConditionalGeneration.from_pretrained("yuriyvnv/whisper-tiny-cv-fully-synthetic-nl")
model.to("cuda")

audio, sr = librosa.load("path/to/dutch_audio.wav", sr=16000)
input_features = processor(audio, sampling_rate=16000, return_tensors="pt").input_features.to("cuda")

predicted_ids = model.generate(input_features)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
print(transcription)

Specifying Language

model.generation_config.language = "nl"
model.generation_config.task = "transcribe"

When to Use This Model

This model is ideal when:

• Maximum in-domain accuracy is required: Best Test WER (24.93%) on Common Voice
• Compute budget is not a constraint: Requires most training steps (1,365)
• Quality filtering is not available: Uses raw synthetic data

Consider filtered alternatives for better efficiency:

• whisper-tiny-high-mixed-nl: 35% fewer steps, slight accuracy tradeoff
• whisper-tiny-mixed-nl: 7% fewer steps, best cross-domain generalization

Quality vs Quantity Analysis

This model demonstrates the tradeoff between data quantity and quality:

Key insight: The unfiltered approach provides only 0.12% absolute WER improvement over mid-high filtering, while requiring 7.5% more training steps. For most applications, filtered approaches offer better compute efficiency.

Limitations

• Model capacity: Whisper-Tiny (39M params) has limited representational power
• Training efficiency: Requires most compute among all configurations
• Noisy training signal: Includes low-quality synthetic samples (13.5% with q < 0.5)
• Domain specificity: Optimized for general Dutch; may underperform on technical domains
• Dialect coverage: Performance may vary across Dutch regional variants

Citation

@article{perezhohin2024enhancing,
  title={Enhancing Automatic Speech Recognition: Effects of Semantic Audio Filtering on Models Performance},
  author={Perezhohin, Yuriy and Santos, Tiago and Costa, Victor and Peres, Fernando and Castelli, Mauro},
  journal={IEEE Access},
  year={2024},
  publisher={IEEE}
}
@article{perezhohin2026wave,
  title={WAVe: Word-aligned verification of synthetic speech for ASR},
  author={Perezhohin, Yuriy and Castelli, Mauro},
  journal={Information Sciences},
  pages={123591},
  year={2026},
  publisher={Elsevier}
}

References

• Base Model: openai/whisper-tiny
• Training Data (Real): mozilla-foundation/common_voice_17_0
• Training Data (Synthetic): yuriyvnv/synthetic_transcript_nl
• Whisper Paper: Robust Speech Recognition via Large-Scale Weak Supervision
• IEEE Access Paper: Enhancing ASR with Semantic Audio Filtering

License

Apache 2.0