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-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MYJOKERML/SDiaReward-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="MYJOKERML/SDiaReward-7B")# Load model directly from transformers import AutoTokenizer, QwenOmniThinkerReward tokenizer = AutoTokenizer.from_pretrained("MYJOKERML/SDiaReward-7B") model = QwenOmniThinkerReward.from_pretrained("MYJOKERML/SDiaReward-7B") - Notebooks
- Google Colab
- Kaggle
File size: 367 Bytes
75eb304 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"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
}
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