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 recipe.yaml from Ar4ikov/VibeVoice-ASR-Streaming-7B-AWQ-W4A16-ASYM-DFlash2: direct link, hf CLI and curl.
- Browser
- Download file 924 Bytes
-
https://huggingface.co/Ar4ikov/VibeVoice-ASR-Streaming-7B-AWQ-W4A16-ASYM-DFlash2/resolve/main/recipe.yaml
- Command line
-
hf download hf://Ar4ikov/VibeVoice-ASR-Streaming-7B-AWQ-W4A16-ASYM-DFlash2/recipe.yaml
-
curl -L -o recipe.yaml https://huggingface.co/Ar4ikov/VibeVoice-ASR-Streaming-7B-AWQ-W4A16-ASYM-DFlash2/resolve/main/recipe.yaml
924 Bytes
| default_stage: | |
| default_modifiers: | |
| AWQModifier: | |
| requires_calibration_data: true | |
| mappings: | |
| - smooth_layer: re:.*input_layernorm$ | |
| balance_layers: ['re:.*q_proj$', 're:.*k_proj$', 're:.*v_proj$'] | |
| activation_hook_target: null | |
| - smooth_layer: re:.*v_proj$ | |
| balance_layers: ['re:.*o_proj$'] | |
| activation_hook_target: null | |
| - smooth_layer: re:.*post_attention_layernorm$ | |
| balance_layers: ['re:.*gate_proj$', 're:.*up_proj$'] | |
| activation_hook_target: null | |
| - smooth_layer: re:.*up_proj$ | |
| balance_layers: ['re:.*down_proj$'] | |
| activation_hook_target: null | |
| offload_device: !!python/object/apply:torch.device [cpu] | |
| duo_scaling: both | |
| n_grid: 40 | |
| QuantizationModifier: | |
| targets: [Linear] | |
| ignore: [lm_head] | |
| scheme: W4A16_ASYM | |
| bypass_divisibility_checks: false | |
| requires_calibration_data: false | |