Audio Classification
Transformers
Safetensors
English
qwen2_5_omni_thinker
reward-model
spoken-dialogue
speech
preference
qwen2_5_omni
modality-awareness
colloquialness
Instructions to use MYJOKERML/SDiaReward-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MYJOKERML/SDiaReward-3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="MYJOKERML/SDiaReward-3B")# Load model directly from transformers import AutoTokenizer, QwenOmniThinkerReward tokenizer = AutoTokenizer.from_pretrained("MYJOKERML/SDiaReward-3B") model = QwenOmniThinkerReward.from_pretrained("MYJOKERML/SDiaReward-3B") - Notebooks
- Google Colab
- Kaggle
| { | |
| "chunk_length": 300, | |
| "dither": 0.0, | |
| "feature_extractor_type": "FastWhisperFeatureExtractor", | |
| "feature_size": 128, | |
| "hop_length": 160, | |
| "n_fft": 400, | |
| "n_samples": 4800000, | |
| "nb_max_frames": 30000, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "processor_class": "OmniRewardProcessor", | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000 | |
| } | |