Orpheus Awq

🚀 Orpheus-3b FT 4bit AWQ量化版本

这是Orpheus-3b FT的4bit AWQ量化版本。我推荐使用lmdeploy，因为它易于安装，并且速度非常快。以下是加载模型、处理音频文件以进行语音克隆和生成语音的代码。

🚀 快速开始

✨ 主要特性

• 提供Orpheus-3b FT的4bit AWQ量化版本。
• 推荐使用易于安装且速度快的lmdeploy。
• 包含加载模型、语音克隆和语音生成的完整代码。

📦 安装指南

使用以下命令安装snac和lmdeploy：

pip install snac lmdeploy

💻 使用示例

基础用法

以下是加载模型的代码：

## Install snac and lmdeploy with pip install snac lmdeploy

from lmdeploy import pipeline, TurbomindEngineConfig, GenerationConfig
from transformers import AutoTokenizer
from snac import SNAC

tp = 1 ## change if you have multiple gpus
cache_max_entry_count = 0.2 ## how much vram is reserved for context

engine_config = TurbomindEngineConfig(model_format='awq', dtype='float16', cache_max_entry_count=cache_max_entry_count, tp=tp, quant_policy=8)
pipe = pipeline("YaTharThShaRma999/orpheus_awq", backend_config=engine_config)
tokeniser = AutoTokenizer.from_pretrained("unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit")
snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").to('cuda:0')

高级用法

以下是将语音文件转换为snac令牌以进行语音克隆的代码：

import librosa
import torch
from IPython.display import Audio
import gc
import torch
from pydub import AudioSegment
tokenizer = tokeniser

my_wav_file_is = "test.mp3" ## path to your reference audio file

and_the_transcript_is = "" ## transcript of the audio file

filename = my_wav_file_is

audio_array, sample_rate = librosa.load(filename)

def tokenise_audio(waveform):
  waveform = torch.from_numpy(waveform).unsqueeze(0)
  waveform = waveform.to(dtype=torch.float32)


  waveform = waveform.unsqueeze(0).to('cuda:0')

  with torch.inference_mode():
    codes = snac_model.encode(waveform)

  all_codes = []
  for i in range(codes[0].shape[1]):
    all_codes.append(codes[0][0][i].item()+128266)
    all_codes.append(codes[1][0][2*i].item()+128266+4096)
    all_codes.append(codes[2][0][4*i].item()+128266+(2*4096))
    all_codes.append(codes[2][0][(4*i)+1].item()+128266+(3*4096))
    all_codes.append(codes[1][0][(2*i)+1].item()+128266+(4*4096))
    all_codes.append(codes[2][0][(4*i)+2].item()+128266+(5*4096))
    all_codes.append(codes[2][0][(4*i)+3].item()+128266+(6*4096))


  return all_codes

myts = tokenise_audio(audio_array) ## the snac tokens

gc.collect()
torch.cuda.empty_cache()

以下是生成语音并使用IPython显示的代码：

from lmdeploy import GenerationConfig
import gc
import torch

### sampling params are heavily experimental, try to experiment with them.
gen_config = GenerationConfig(top_p=0.7,
                              top_k=50,
                              temperature=0.2,
                              max_new_tokens=1024,
                              min_new_tokens=30,
                              stop_token_ids=[128009, 128001, 49158, 128258],
                              repetition_penalty=2.0,
                              skip_special_tokens=False,
                              do_sample=True,
                              min_p=0.6)

prompt = and_the_transcript_is + "<laugh> So um hey, like what's up??" ## put prompt here

voice_name = "zac" ## experimental, might be removed or not
response2 = pipe([f"<custom_token_3><|begin_of_text|>{voice_name}: {prompt}<|eot_id|><custom_token_4><custom_token_5><custom_token_1>" + tokeniser.decode(myts)], gen_config=gen_config)
gc.collect()
torch.cuda.empty_cache()

generated_ids = tokeniser.encode(response2[0].text, return_tensors='pt',  add_special_tokens=False)

token_to_find = 128257
token_to_remove = 128258

token_indices = (generated_ids == token_to_find).nonzero(as_tuple=True)

if len(token_indices[1]) > 0:
    last_occurrence_idx = token_indices[1][-1].item()
    cropped_tensor = generated_ids[:, last_occurrence_idx+1:]
else:
    cropped_tensor = generated_ids
mask = cropped_tensor != token_to_remove

processed_rows = []

for row in cropped_tensor:
    masked_row = row[row != token_to_remove]
    processed_rows.append(masked_row)

code_lists = []

for row in processed_rows:
    row_length = row.size(0)
    new_length = (row_length // 7) * 7
    trimmed_row = row[:new_length]
    trimmed_row = [t - 128266 for t in trimmed_row]
    code_lists.append(trimmed_row)


def redistribute_codes(code_list):
  layer_1 = []
  layer_2 = []
  layer_3 = []
  for i in range((len(code_list)+1)//7):
    layer_1.append(code_list[7*i])
    layer_2.append(code_list[7*i+1]-4096)
    layer_3.append(code_list[7*i+2]-(2*4096))
    layer_3.append(code_list[7*i+3]-(3*4096))
    layer_2.append(code_list[7*i+4]-(4*4096))
    layer_3.append(code_list[7*i+5]-(5*4096))
    layer_3.append(code_list[7*i+6]-(6*4096))
  codes = [torch.tensor(layer_1).unsqueeze(0).to('cuda:0'),
         torch.tensor(layer_2).unsqueeze(0).to('cuda:0'),
         torch.tensor(layer_3).unsqueeze(0).to('cuda:0')]

  audio_hat = snac_model.decode(codes)
  return audio_hat

my_samples = []
for code_list in code_lists:
  samples = redistribute_codes(code_list)
  my_samples.append(samples)
from IPython.display import display, Audio

display(Audio(samples.detach().squeeze().to("cpu").numpy(), rate=24000))

del my_samples,samples, code_lists, mask, cropped_tensor, processed_rows
gc.collect()
torch.cuda.empty_cache()

📄 许可证

本项目采用Apache-2.0许可证。