.ipynb_checkpoints/main-checkpoint.py

import gradio as gr
import torch
import torchaudio
from transformers import AutoTokenizer, AutoModelForCausalLM
from speechtokenizer import SpeechTokenizer
from audiotools import AudioSignal
import bitsandbytes as bnb  # Import bitsandbytes for INT8 quantization
import numpy as np
from uuid import uuid4

# Load the necessary models and tokenizers
model_path = "Vikhrmodels/llama_asr_tts_24000"
tokenizer = AutoTokenizer.from_pretrained(model_path, cache_dir=".")
# Специальные токены
start_audio_token = "<soa>"
end_audio_token = "<eoa>"
end_sequence_token = "<eos>"

# Константы
n_codebooks = 3
max_seq_length = 1024
top_k = 20

from safetensors.torch import load_file

def convert_to_16_bit_wav(data):
    # Based on: https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.wavfile.write.html
    # breakpoint()
    if data.dtype == np.float32:
        # warnings.warn(
        #     "Audio data is not in 16-bit integer format."
        #     "Trying to convert to 16-bit int format."
        # )
        data = data / np.abs(data).max()
        data = data * 32767
        data = data.astype(np.int16)
    elif data.dtype == np.int32:
        # warnings.warn(
        #     "Audio data is not in 16-bit integer format."
        #     "Trying to convert to 16-bit int format."
        # )
        data = data / 65538
        data = data.astype(np.int16)
    elif data.dtype == np.int16:
        pass
    elif data.dtype == np.uint8:
        # warnings.warn(
        #     "Audio data is not in 16-bit integer format."
        #     "Trying to convert to 16-bit int format."
        # )
        data = data * 257 - 32768
        data = data.astype(np.int16)
    else:
        raise ValueError("Audio data cannot be converted to " "16-bit int format.")
    return data

# Load the model with INT8 quantization
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    cache_dir=".",
    load_in_8bit=True,  # Enable loading in INT8
    device_map="auto"  # Automatically map model to available devices
)

# Configurations for Speech Tokenizer
config_path = "audiotokenizer/speechtokenizer_hubert_avg_config.json"
ckpt_path = "audiotokenizer/SpeechTokenizer.pt"
quantizer = SpeechTokenizer.load_from_checkpoint(config_path, ckpt_path)
quantizer.eval()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Перемещение всех слоев квантизатора на устройство и их заморозка
def freeze_entire_model(model):
    for n, p in model.named_parameters():
        p.requires_grad = False
    return model

for n, child in quantizer.named_children():
    child.to(device)
    child = freeze_entire_model(child)

# Функция для создания токенов заполнения для аудио
def get_audio_padding_tokens(quantizer):
    audio = torch.zeros((1, 1, 1)).to(device)
    codes = quantizer.encode(audio)
    del audio
    torch.cuda.empty_cache()
    return {"audio_tokens": codes.squeeze(1)}

# Функция для декодирования аудио из токенов
def decode_audio(tokens, quantizer, pad_tokens, n_original_tokens):
    start = torch.nonzero(tokens == tokenizer(start_audio_token)["input_ids"][-1])
    end = torch.nonzero(tokens == tokenizer(end_audio_token)["input_ids"][-1])
    start = start[0, -1] + 1 if len(start) else 0
    end = end[0, -1] if len(end) else tokens.shape[-1]

    audio_tokens = tokens[start:end] % n_original_tokens
    reminder = audio_tokens.shape[-1] % n_codebooks

    if reminder:
        audio_tokens = torch.cat([audio_tokens, pad_tokens[reminder:n_codebooks]], dim=0)

    transposed = audio_tokens.view(-1, n_codebooks).t()
    codes = transposed.view(n_codebooks, 1, -1).to(device)

    audio = quantizer.decode(codes).squeeze(0)
    torch.cuda.empty_cache()
    xp = str(uuid4())+'.wav'
    AudioSignal(audio.detach().cpu().numpy(),quantizer.sample_rate).write(xp)
    return xp


# Пример использования

# Функция инференса для текста на входе и аудио на выходе
def infer_text_to_audio(text, model, tokenizer, quantizer, max_seq_length=1024, top_k=20):
    text_tokenized = tokenizer(text, return_tensors="pt")
    text_input_tokens = text_tokenized["input_ids"].to(device)

    soa = tokenizer(start_audio_token, return_tensors="pt")["input_ids"][:, -1:].to(device)
    eoa = tokenizer(end_audio_token, return_tensors="pt")["input_ids"][:, -1:].to(device)

    text_tokens = torch.cat([text_input_tokens, soa], dim=1)
    attention_mask = torch.ones(text_tokens.size(), device=device)

    output_audio_tokens = model.generate(text_tokens, attention_mask=attention_mask, max_new_tokens=max_seq_length, top_k=top_k, do_sample=True)

    padding_tokens = get_audio_padding_tokens(quantizer)["audio_tokens"].to(device)
    audio_signal = decode_audio(output_audio_tokens[0], quantizer, padding_tokens.t()[0], len(tokenizer) - 1024)

    return audio_signal

# Функция инференса для аудио на входе и текста на выходе
def infer_audio_to_text(audio_path, model, tokenizer, quantizer, max_seq_length=1024, top_k=20):
    audio_data, sample_rate = torchaudio.load(audio_path)

    audio = audio_data.view(1, 1, -1).float().to(device)
    codes = quantizer.encode(audio)
    n_codebooks_a = 1
    raw_audio_tokens = codes[:, :n_codebooks_a] + len(tokenizer) - 1024

    soa = tokenizer(start_audio_token, return_tensors="pt")["input_ids"][:, -1:].to(device)
    eoa = tokenizer(end_audio_token, return_tensors="pt")["input_ids"][:, -1:].to(device)
    audio_tokens = torch.cat([soa, raw_audio_tokens.view(1, -1), eoa], dim=1)

    attention_mask = torch.ones(audio_tokens.size(), device=device)

    output_text_tokens = model.generate(audio_tokens, attention_mask=attention_mask, max_new_tokens=max_seq_length, top_k=top_k, do_sample=True)

    output_text_tokens = output_text_tokens.cpu()[0]
    output_text_tokens = output_text_tokens[output_text_tokens < tokenizer(start_audio_token)["input_ids"][-1]]
    decoded_text = tokenizer.decode(output_text_tokens, skip_special_tokens=True)

    return decoded_text

# Functions for inference
def infer_text_to_audio_gr(text):
    audio_signal = infer_text_to_audio(text.strip().upper(), model, tokenizer, quantizer)
    return audio_signal

def infer_audio_to_text_gr(audio_path):
    generated_text = infer_audio_to_text(audio_path, model, tokenizer, quantizer)
    return generated_text

# Gradio Interface
text_to_audio_interface = gr.Interface(
    fn=infer_text_to_audio_gr,
    inputs=gr.Textbox(label="Input Text"),
    outputs=gr.Audio(label="Аудио Ответ"),
    title="T2S",
    description="Модель в режиме ответа в аудио",
    allow_flagging='never',
)

audio_to_text_interface = gr.Interface(
    fn=infer_audio_to_text_gr,
    inputs=gr.Audio(type="filepath", label="Input Audio"),
    outputs=gr.Textbox(label="Текстовый ответ"),
    title="S2T",
    description="Модель в режиме ответа в тексте",
    allow_flagging='never'
)

# Launch Gradio App
demo = gr.TabbedInterface([text_to_audio_interface, audio_to_text_interface], ["Текст - Аудио", "Аудио - Текст"])
demo.launch(share=True)