Automatic Speech Recognition
Safetensors
VibeVoice
vibevoice.c
speculative-decoding
dflash
awq
compressed-tensors
speech-recognition
4-bit precision
Instructions to use Ar4ikov/VibeVoice-ASR-Streaming-7B-AWQ-W4A16-ASYM-DFlash2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- VibeVoice
How to use Ar4ikov/VibeVoice-ASR-Streaming-7B-AWQ-W4A16-ASYM-DFlash2 with VibeVoice:
import torch, soundfile as sf, librosa, numpy as np from vibevoice.processor.vibevoice_processor import VibeVoiceProcessor from vibevoice.modular.modeling_vibevoice_inference import VibeVoiceForConditionalGenerationInference # Load voice sample (should be 24kHz mono) voice, sr = sf.read("path/to/voice_sample.wav") if voice.ndim > 1: voice = voice.mean(axis=1) if sr != 24000: voice = librosa.resample(voice, sr, 24000) processor = VibeVoiceProcessor.from_pretrained("Ar4ikov/VibeVoice-ASR-Streaming-7B-AWQ-W4A16-ASYM-DFlash2") model = VibeVoiceForConditionalGenerationInference.from_pretrained( "Ar4ikov/VibeVoice-ASR-Streaming-7B-AWQ-W4A16-ASYM-DFlash2", torch_dtype=torch.bfloat16 ).to("cuda").eval() model.set_ddpm_inference_steps(5) inputs = processor(text=["Speaker 0: Hello!\nSpeaker 1: Hi there!"], voice_samples=[[voice]], return_tensors="pt") audio = model.generate(**inputs, cfg_scale=1.3, tokenizer=processor.tokenizer).speech_outputs[0] sf.write("output.wav", audio.cpu().numpy().squeeze(), 24000) - Notebooks
- Google Colab
- Kaggle
Download compression_report.json from Ar4ikov/VibeVoice-ASR-Streaming-7B-AWQ-W4A16-ASYM-DFlash2: direct link, hf CLI and curl.
- Browser
- Download file 1 kB
-
https://huggingface.co/Ar4ikov/VibeVoice-ASR-Streaming-7B-AWQ-W4A16-ASYM-DFlash2/resolve/463a3ca66fc35a20043753f92984fc66f858926a/compression_report.json
- Command line
-
hf download hf://Ar4ikov/VibeVoice-ASR-Streaming-7B-AWQ-W4A16-ASYM-DFlash2@463a3ca66fc35a20043753f92984fc66f858926a/compression_report.json
-
curl -L -o compression_report.json https://huggingface.co/Ar4ikov/VibeVoice-ASR-Streaming-7B-AWQ-W4A16-ASYM-DFlash2/resolve/463a3ca66fc35a20043753f92984fc66f858926a/compression_report.json
1 kB
| { | |
| "source_revision": "60d858b518b4e19d404af3737f848fc185b30177", | |
| "source_safetensors_bytes": 17348198658, | |
| "output_safetensors_bytes": 7000335408, | |
| "compression_ratio": 2.478195350207711, | |
| "quantized_projections": 196, | |
| "method": "Activation-aware AWQ using llm-compressor; exact layout repack into AWQ GEMM", | |
| "lm_head": "BF16", | |
| "scheme": "W4A16_ASYM", | |
| "group_size": 128, | |
| "duo_scaling": "both", | |
| "grid_points": 40, | |
| "calibration_samples": 256, | |
| "audio_calibration_samples": 128, | |
| "text_calibration_samples": 128, | |
| "calibration_stats": { | |
| "audio_sessions": 128, | |
| "audio_rows_mean": 944.7734375, | |
| "audio_rows_total": 120931, | |
| "truncated_at_2048": 0 | |
| }, | |
| "max_sequence_length": 2048, | |
| "packing_validation": "All integer weight and zero-point codes round-trip exactly; all scale values preserved exactly", | |
| "pruning": { | |
| "removed_prefixes": [ | |
| "model.acoustic_tokenizer.decoder." | |
| ], | |
| "removed_bf16_bytes": 687391938, | |
| "speech_bytes": 1429418752 | |
| } | |
| } |