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Model: yuriyvnv/whisper-large-v3-high-mixed-nl
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+language:
+- nl
+base_model: openai/whisper-large-v3
+tags:
+- automatic-speech-recognition
+- whisper
+- dutch
+- speech
+- audio
+- synthetic-data
+- asr
+- hf-asr-leaderboard
+datasets:
+- mozilla-foundation/common_voice_17_0
+- yuriyvnv/synthetic_transcript_nl
+model-index:
+- name: whisper-large-v3-high-mixed-nl
+  results:
+  - task:
+      type: automatic-speech-recognition
+      name: Automatic Speech Recognition
+    dataset:
+      name: Common Voice 17.0 (Dutch)
+      type: mozilla-foundation/common_voice_17_0
+      config: nl
+      split: test
+    metrics:
+    - type: wer
+      value: 4.43
+      name: Test WER
+  - task:
+      type: automatic-speech-recognition
+      name: Automatic Speech Recognition
+    dataset:
+      name: Multilingual LibriSpeech (Dutch)
+      type: facebook/multilingual_librispeech
+      config: dutch
+      split: test
+    metrics:
+    - type: wer
+      value: 20.29
+      name: Test WER (MLS)
+pipeline_tag: automatic-speech-recognition
+library_name: transformers
+---
+
+# Whisper-Large-v3 Dutch - High-Quality Filtered Synthetic Data
+
+This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) for Dutch automatic speech recognition (ASR). It was trained on Common Voice 17.0 Dutch combined with **WAVe-filtered high-quality synthetic speech data only** using a strict threshold (q ≥ 0.8).
+
+## Introduction
+
+### How the Data Was Created
+
+The training data combines real speech from Common Voice 17.0 with synthetic speech generated through a two-stage pipeline:
+
+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. **Quality Filtering with WAVe**: Raw synthetic speech often contains defects such as mispronunciations, omitted words, or prosodic anomalies. To address this, we applied **WAVe (Word-Aligned Verification)**, a model that assesses audio-text alignment at the word level rather than the sentence level. WAVe uses multi-head attention to align each word to its corresponding audio frames and assigns per-word confidence scores via a GLU-based scorer. For this model, only samples scoring above the strict threshold (q ≥ 0.8) were retained, resulting in 10,555 high-quality synthetic samples.
+
+### How the Model Was Created
+
+The model was fine-tuned from `openai/whisper-large-v3` using the Hugging Face Transformers library with the following approach:
+
+1. **Mixed Training**: Combined 34,952 real speech samples from Common Voice 17.0 Dutch with 10,555 strictly WAVe-filtered high-quality synthetic samples (45,507 total).
+
+2. **Optimization**: Trained for 5 epochs with a learning rate of 5e-6, 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 350 with a validation loss of 0.0552.
+
+This high-quality filtering approach achieves **35% reduction in training steps** compared to using all synthetic data, while maintaining excellent ASR performance.
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| **Base Model** | openai/whisper-large-v3 |
+| **Language** | Dutch (nl) |
+| **Task** | Automatic Speech Recognition (transcribe) |
+| **Parameters** | 1550M |
+| **Training Data** | Common Voice 17.0 + High-Quality Synthetic (q ≥ 0.8) |
+| **Total Training Samples** | 45,507 |
+| **Sampling Rate** | 16kHz |
+
+## Evaluation Results
+
+### This Model (whisper-large-v3-high-mixed-nl)
+
+| Metric | Value |
+|--------|-------|
+| **Validation Loss** | 0.0520 |
+| **Validation WER** | 3.57% |
+| **Test WER (Common Voice)** | 4.43% |
+| **Test WER (MLS)** | 20.29% |
+| **Best Checkpoint** | Step 350 |
+| **Max Training Steps** | 890 |
+
+### Comparison with Other Training Configurations (Whisper-Large-v3 Dutch)
+
+| Training Data | Max Steps | Val Loss | Val WER | Test WER (CV) | Test WER (MLS) |
+|---------------|-----------|----------|---------|---------------|----------------|
+| Common Voice Only | 680 | 0.0549 | 3.56% | 4.39% | 22.43% |
+| **High-Quality Filtered + CV** | **890** | **0.0520** | **3.57%** | **4.43%** | **20.29%** |
+| Mid-High Quality Filtered + CV | 1,270 | 0.0570 | 3.63% | 4.48% | 17.25% |
+| All Synthetic + CV (Unfiltered) | 1,365 | 0.0560 | 3.61% | 4.44% | 17.02% |
+
+### Key Performance Highlights
+
+- **Most efficient training**: Only 890 max steps (35% fewer than unfiltered)
+- **Best validation loss** (0.0520) among all Whisper-Large-v3 Dutch configurations
+- **Competitive in-domain performance**: 4.43% Test WER on Common Voice
+- **9.5% relative improvement** on MLS benchmark vs baseline (20.29% vs 22.43%)
+- **Best quality-to-compute ratio**: Strong results with only top-tier synthetic data (30.2%)
+
+## Training Data
+
+### Dataset Composition
+
+| Source | Samples | Description |
+|--------|---------|-------------|
+| [Common Voice 17.0 Dutch](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0) | 34,952 | Real speech from Mozilla's crowdsourced dataset |
+| [Synthetic Transcript NL](https://huggingface.co/datasets/yuriyvnv/synthetic_transcript_nl) (q ≥ 0.8) | 10,555 | Strictly WAVe-filtered TTS audio (high quality only) |
+| **Total** | **45,507** | |
+
+### Synthetic Data Generation Pipeline
+
+The synthetic dataset ([yuriyvnv/synthetic_transcript_nl](https://huggingface.co/datasets/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. **Quality Filtering**: WAVe model with strict threshold q ≥ 0.8 (high quality only)
+
+### WAVe Quality Distribution (Dutch Synthetic Data)
+
+| Quality Level | Samples | Percentage | Used in This Model |
+|--------------|---------|------------|-------------------|
+| High (q ≥ 0.8) | 10,555 | 30.2% | ✓ |
+| Medium (0.5 ≤ q < 0.8) | 19,627 | 56.2% | ✗ |
+| Low (q < 0.5) | 4,716 | 13.5% | ✗ |
+
+This strict threshold retains only the top 30.2% of synthetic samples, prioritizing quality over quantity for maximum training efficiency.
+
+## Training Procedure
+
+### Hyperparameters
+
+| Parameter | Value |
+|-----------|-------|
+| Learning Rate | 5e-6 |
+| Batch Size (Global) | 256 |
+| Warmup Steps | 200 |
+| Max Epochs | 5 |
+| Precision | BF16 |
+| Optimizer | AdamW (fused) |
+| Eval Steps | 50 |
+| Metric for Best Model | eval_loss |
+
+### Training Infrastructure
+
+- **GPU**: NVIDIA H200 (140GB VRAM)
+- **Operating System**: Ubuntu 22.04
+- **Framework**: Hugging Face Transformers
+
+### Training Curve
+
+```
+Step  100: val_loss = 0.0588
+Step  200: val_loss = 0.0562
+Step  250: val_loss = 0.0561
+Step  350: val_loss = 0.0552 ← Best checkpoint
+Step  500: val_loss = 0.0601
+Step  650: val_loss = 0.0627
+Step  850: val_loss = 0.0680
+```
+
+## Usage
+
+### Transcription Pipeline
+
+```python
+from transformers import pipeline
+
+transcriber = pipeline(
+    "automatic-speech-recognition",
+    model="yuriyvnv/whisper-large-v3-high-mixed-nl",
+    device="cuda"
+)
+
+result = transcriber("path/to/dutch_audio.wav")
+print(result["text"])
+```
+
+### Direct Model Usage
+
+```python
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+import librosa
+
+processor = WhisperProcessor.from_pretrained("yuriyvnv/whisper-large-v3-high-mixed-nl")
+model = WhisperForConditionalGeneration.from_pretrained("yuriyvnv/whisper-large-v3-high-mixed-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
+
+```python
+model.generation_config.language = "nl"
+model.generation_config.task = "transcribe"
+```
+
+## Methodology
+
+This model leverages **WAVe (Word-Aligned Verification)**, a word-level quality assessment method for filtering synthetic speech data. Unlike sentence-level filtering approaches, WAVe:
+
+- Aligns each word to its corresponding audio frames using multi-head attention
+- Assigns per-word confidence scores via a GLU-based scorer
+- Detects localized synthesis errors (mispronunciations, omitted words, prosodic anomalies)
+- Achieves **6.5% improvement** over sentence-level filtering methods
+
+The strict threshold (q ≥ 0.8) retains only the top 30.2% of synthetic samples, prioritizing quality over quantity for maximum training efficiency.
+
+## When to Use This Model
+
+This model is ideal when:
+- **Compute resources are limited**: 35% fewer training steps than unfiltered approaches
+- **Quick fine-tuning is needed**: Smaller dataset (45,507 samples) enables faster iteration
+- **Best validation performance required**: Achieves lowest validation loss (0.0520)
+- **Quality over quantity**: Only top-tier synthetic data (30.2%) for clean training signal
+
+Consider other variants based on your needs:
+- [whisper-large-v3-mixed-cv-nl](https://huggingface.co/yuriyvnv/whisper-large-v3-mixed-cv-nl): Better cross-domain performance with more data
+- [whisper-large-v3-cv-fully-synthetic-nl](https://huggingface.co/yuriyvnv/whisper-large-v3-cv-fully-synthetic-nl): Best cross-domain generalization (17.02% MLS)
+
+## Limitations
+
+- **Domain specificity**: Optimized for general Dutch; may underperform on technical domains
+- **Acoustic conditions**: Trained on clean speech; noise robustness not guaranteed
+- **Dialect coverage**: Performance may vary across Dutch regional variants
+
+## Citation
+
+```bibtex
+@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}
+}
+```
+
+## References
+
+- **Base Model**: [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3)
+- **Training Data (Real)**: [mozilla-foundation/common_voice_17_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0)
+- **Training Data (Synthetic)**: [yuriyvnv/synthetic_transcript_nl](https://huggingface.co/datasets/yuriyvnv/synthetic_transcript_nl)
+- **Whisper Paper**: [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356)
+- **IEEE Access Paper**: [Enhancing ASR with Semantic Audio Filtering](https://ieeexplore.ieee.org/document/10720758)
+
+## License
+
+Apache 2.0

config.json

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+  "max_target_positions": 448,
+  "median_filter_width": 7,
+  "model_type": "whisper",
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+  "num_mel_bins": 128,
+  "pad_token_id": 50256,
+  "scale_embedding": false,
+  "torch_dtype": "float32",
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+  "use_cache": false,
+  "use_weighted_layer_sum": false,
+  "vocab_size": 51866
+}

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special_tokens_map.json

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+    "<|en|>",
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+    "<|de|>",
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+    "<|ru|>",
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