app.py

import gradio as gr
import subprocess
import os 
import shutil
import tempfile
import spaces
from transformers import AutoTokenizer, AutoModelForCausalLM, LogitsProcessor, LogitsProcessorList
import torch

is_shared_ui = True if "innova-ai/YuE-music-generator-demo" in os.environ['SPACE_ID'] else False

# Install required package
def install_flash_attn():
    try:
        print("Installing flash-attn...")
        # Install flash attention
        subprocess.run(
            "pip install flash-attn --no-build-isolation",
            env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
            shell=True,
        )
        print("flash-attn installed successfully!")
    except subprocess.CalledProcessError as e:
        print(f"Failed to install flash-attn: {e}")
        exit(1)

# Install flash-attn
install_flash_attn()

from huggingface_hub import snapshot_download 

# Create xcodec_mini_infer folder
folder_path = './inference/xcodec_mini_infer'

# Create the folder if it doesn't exist
if not os.path.exists(folder_path):
    os.mkdir(folder_path)
    print(f"Folder created at: {folder_path}")
else:
    print(f"Folder already exists at: {folder_path}")

snapshot_download(
    repo_id = "m-a-p/xcodec_mini_infer",
    local_dir = "./inference/xcodec_mini_infer"
)

# Change to the "inference" directory
inference_dir = "./inference"
try:
    os.chdir(inference_dir)
    print(f"Changed working directory to: {os.getcwd()}")
except FileNotFoundError:
    print(f"Directory not found: {inference_dir}")
    exit(1)

def empty_output_folder(output_dir):
    # List all files in the output directory
    files = os.listdir(output_dir)
    
    # Iterate over the files and remove them
    for file in files:
        file_path = os.path.join(output_dir, file)
        try:
            if os.path.isdir(file_path):
                # If it's a directory, remove it recursively
                shutil.rmtree(file_path)
            else:
                # If it's a file, delete it
                os.remove(file_path)
        except Exception as e:
            print(f"Error deleting file {file_path}: {e}")

# Function to create a temporary file with string content
def create_temp_file(content, prefix, suffix=".txt"):
    temp_file = tempfile.NamedTemporaryFile(delete=False, mode="w", prefix=prefix, suffix=suffix)
    # Ensure content ends with newline and normalize line endings
    content = content.strip() + "\n\n"  # Add extra newline at end
    content = content.replace("\r\n", "\n").replace("\r", "\n")
    temp_file.write(content)
    temp_file.close()
    
    # Debug: Print file contents
    print(f"\nContent written to {prefix}{suffix}:")
    print(content)
    print("---")
    
    return temp_file.name

def get_last_mp3_file(output_dir):
    # List all files in the output directory
    files = os.listdir(output_dir)
    
    # Filter only .mp3 files
    mp3_files = [file for file in files if file.endswith('.mp3')]
    
    if not mp3_files:
        print("No .mp3 files found in the output folder.")
        return None
    
    # Get the full path for the mp3 files
    mp3_files_with_path = [os.path.join(output_dir, file) for file in mp3_files]
    
    # Sort the files based on the modification time (most recent first)
    mp3_files_with_path.sort(key=lambda x: os.path.getmtime(x), reverse=True)
    
    # Return the most recent .mp3 file
    return mp3_files_with_path[0]

device = torch.device(f"cuda" if torch.cuda.is_available() else "cpu")

model = AutoModelForCausalLM.from_pretrained(
    "m-a-p/YuE-s1-7B-anneal-en-cot", 
    torch_dtype=torch.float16,
    attn_implementation="flash_attention_2", # To enable flashattn, you have to install flash-attn
    )
model.to(device)
model.eval()

import os
import sys
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'xcodec_mini_infer'))
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'xcodec_mini_infer', 'descriptaudiocodec'))
import argparse
import torch
import numpy as np
import json
from omegaconf import OmegaConf
import torchaudio
from torchaudio.transforms import Resample
import soundfile as sf

import uuid
from tqdm import tqdm
from einops import rearrange
from codecmanipulator import CodecManipulator
from mmtokenizer import _MMSentencePieceTokenizer
from transformers import AutoTokenizer, AutoModelForCausalLM, LogitsProcessor, LogitsProcessorList
import glob
import time
import copy
from collections import Counter
from models.soundstream_hubert_new import SoundStream
from vocoder import build_codec_model, process_audio
from post_process_audio import replace_low_freq_with_energy_matched
import re

def generate_music(
    stage1_model="m-a-p/YuE-s1-7B-anneal-en-cot",
    max_new_tokens=3000,
    run_n_segments=2,
    genre_txt=None,
    lyrics_txt=None,
    use_audio_prompt=False,
    audio_prompt_path="",
    prompt_start_time=0.0,
    prompt_end_time=30.0,
    output_dir="./output",
    keep_intermediate=False,
    disable_offload_model=False,
    cuda_idx=0,
    basic_model_config='./xcodec_mini_infer/final_ckpt/config.yaml',
    resume_path='./xcodec_mini_infer/final_ckpt/ckpt_00360000.pth',
    config_path='./xcodec_mini_infer/decoders/config.yaml',
    vocal_decoder_path='./xcodec_mini_infer/decoders/decoder_131000.pth',
    inst_decoder_path='./xcodec_mini_infer/decoders/decoder_151000.pth',
    rescale=False,
):
    if use_audio_prompt and not audio_prompt_path:
        raise FileNotFoundError("Please offer audio prompt filepath using '--audio_prompt_path', when you enable 'use_audio_prompt'!")
    
    model = stage1_model
    cuda_idx = cuda_idx
    max_new_tokens = max_new_tokens
    stage1_output_dir = os.path.join(output_dir, f"stage1")
    os.makedirs(stage1_output_dir, exist_ok=True)

    # load tokenizer and model
    device = torch.device(f"cuda:{cuda_idx}" if torch.cuda.is_available() else "cpu")

    # Now you can use `device` to move your tensors or models to the GPU (if available)
    print(f"Using device: {device}")

    mmtokenizer = _MMSentencePieceTokenizer("./mm_tokenizer_v0.2_hf/tokenizer.model")

    codectool = CodecManipulator("xcodec", 0, 1)
    model_config = OmegaConf.load(basic_model_config)
    codec_model = eval(model_config.generator.name)(**model_config.generator.config).to(device)
    parameter_dict = torch.load(resume_path, map_location='cpu')
    codec_model.load_state_dict(parameter_dict['codec_model'])
    codec_model.to(device)
    codec_model.eval()

    class BlockTokenRangeProcessor(LogitsProcessor):
        def __init__(self, start_id, end_id):
            self.blocked_token_ids = list(range(start_id, end_id))

        def __call__(self, input_ids, scores):
            scores[:, self.blocked_token_ids] = -float("inf")
            return scores

    def load_audio_mono(filepath, sampling_rate=16000):
        audio, sr = torchaudio.load(filepath)
        # Convert to mono
        audio = torch.mean(audio, dim=0, keepdim=True)
        # Resample if needed
        if sr != sampling_rate:
            resampler = Resample(orig_freq=sr, new_freq=sampling_rate)
            audio = resampler(audio)
        return audio

    def split_lyrics(lyrics):
        pattern = r"\[(\w+)\](.*?)\n(?=\[|\Z)"
        segments = re.findall(pattern, lyrics, re.DOTALL)
        structured_lyrics = [f"[{seg[0]}]\n{seg[1].strip()}\n\n" for seg in segments]
        return structured_lyrics

    # Call the function and print the result
    stage1_output_set = []
    # Tips:
    # genre tags support instrumental，genre，mood，vocal timbr and vocal gender
    # all kinds of tags are needed
    with open(genre_txt) as f:
        genres = f.read().strip()
    with open(lyrics_txt) as f:
        lyrics = split_lyrics(f.read())
    # intruction
    full_lyrics = "\n".join(lyrics)
    prompt_texts = [f"Generate music from the given lyrics segment by segment.\n[Genre] {genres}\n{full_lyrics}"]
    prompt_texts += lyrics


    random_id = uuid.uuid4()
    output_seq = None
    # Here is suggested decoding config
    top_p = 0.93
    temperature = 1.0
    repetition_penalty = 1.2
    # special tokens
    start_of_segment = mmtokenizer.tokenize('[start_of_segment]')
    end_of_segment = mmtokenizer.tokenize('[end_of_segment]')

    raw_output = None

    # Format text prompt
    run_n_segments = min(run_n_segments+1, len(lyrics))

    print(list(enumerate(tqdm(prompt_texts[:run_n_segments]))))

    for i, p in enumerate(tqdm(prompt_texts[:run_n_segments])):
        section_text = p.replace('[start_of_segment]', '').replace('[end_of_segment]', '')
        guidance_scale = 1.5 if i <=1 else 1.2
        if i==0:
            continue
        if i==1:
            if use_audio_prompt:
                audio_prompt = load_audio_mono(audio_prompt_path)
                audio_prompt.unsqueeze_(0)
                with torch.no_grad():
                    raw_codes = codec_model.encode(audio_prompt.to(device), target_bw=0.5)
                raw_codes = raw_codes.transpose(0, 1)
                raw_codes = raw_codes.cpu().numpy().astype(np.int16)
                # Format audio prompt
                code_ids = codectool.npy2ids(raw_codes[0])
                audio_prompt_codec = code_ids[int(prompt_start_time *50): int(prompt_end_time *50)] # 50 is tps of xcodec
                audio_prompt_codec_ids = [mmtokenizer.soa] + codectool.sep_ids + audio_prompt_codec + [mmtokenizer.eoa]
                sentence_ids = mmtokenizer.tokenize("[start_of_reference]") +  audio_prompt_codec_ids + mmtokenizer.tokenize("[end_of_reference]")
                head_id = mmtokenizer.tokenize(prompt_texts[0]) + sentence_ids
            else:
                head_id = mmtokenizer.tokenize(prompt_texts[0])
            prompt_ids = head_id + start_of_segment + mmtokenizer.tokenize(section_text) + [mmtokenizer.soa] + codectool.sep_ids
        else:
            prompt_ids = end_of_segment + start_of_segment + mmtokenizer.tokenize(section_text) + [mmtokenizer.soa] + codectool.sep_ids

        prompt_ids = torch.as_tensor(prompt_ids).unsqueeze(0).to(device) 
        input_ids = torch.cat([raw_output, prompt_ids], dim=1) if i > 1 else prompt_ids
        # Use window slicing in case output sequence exceeds the context of model
        max_context = 16384-max_new_tokens-1
        if input_ids.shape[-1] > max_context:
            print(f'Section {i}: output length {input_ids.shape[-1]} exceeding context length {max_context}, now using the last {max_context} tokens.')
            input_ids = input_ids[:, -(max_context):]
        with torch.no_grad():
            output_seq = model.generate(
                input_ids=input_ids, 
                max_new_tokens=max_new_tokens, 
                min_new_tokens=100, 
                do_sample=True, 
                top_p=top_p,
                temperature=temperature, 
                repetition_penalty=repetition_penalty, 
                eos_token_id=mmtokenizer.eoa,
                pad_token_id=mmtokenizer.eoa,
                logits_processor=LogitsProcessorList([BlockTokenRangeProcessor(0, 32002), BlockTokenRangeProcessor(32016, 32016)]),
                guidance_scale=guidance_scale,
                )
            if output_seq[0][-1].item() != mmtokenizer.eoa:
                tensor_eoa = torch.as_tensor([[mmtokenizer.eoa]]).to(model.device)
                output_seq = torch.cat((output_seq, tensor_eoa), dim=1)
        if i > 1:
            raw_output = torch.cat([raw_output, prompt_ids, output_seq[:, input_ids.shape[-1]:]], dim=1)
        else:
            raw_output = output_seq
        print(len(raw_output))

    # save raw output and check sanity
    ids = raw_output[0].cpu().numpy()
    soa_idx = np.where(ids == mmtokenizer.soa)[0].tolist()
    eoa_idx = np.where(ids == mmtokenizer.eoa)[0].tolist()
    if len(soa_idx)!=len(eoa_idx):
        raise ValueError(f'invalid pairs of soa and eoa, Num of soa: {len(soa_idx)}, Num of eoa: {len(eoa_idx)}')

    vocals = []
    instrumentals = []
    range_begin = 1 if use_audio_prompt else 0
    for i in range(range_begin, len(soa_idx)):
        codec_ids = ids[soa_idx[i]+1:eoa_idx[i]]
        if codec_ids[0] == 32016:
            codec_ids = codec_ids[1:]
        codec_ids = codec_ids[:2 * (codec_ids.shape[0] // 2)]
        vocals_ids = codectool.ids2npy(rearrange(codec_ids,"(n b) -> b n", b=2)[0])
        vocals.append(vocals_ids)
        instrumentals_ids = codectool.ids2npy(rearrange(codec_ids,"(n b) -> b n", b=2)[1])
        instrumentals.append(instrumentals_ids)
    vocals = np.concatenate(vocals, axis=1)
    instrumentals = np.concatenate(instrumentals, axis=1)
    vocal_save_path = os.path.join(stage1_output_dir, f"cot_{genres.replace(' ', '-')}_tp{top_p}_T{temperature}_rp{repetition_penalty}_maxtk{max_new_tokens}_vocal_{random_id}".replace('.', '@')+'.npy')
    inst_save_path = os.path.join(stage1_output_dir, f"cot_{genres.replace(' ', '-')}_tp{top_p}_T{temperature}_rp{repetition_penalty}_maxtk{max_new_tokens}_instrumental_{random_id}".replace('.', '@')+'.npy')
    np.save(vocal_save_path, vocals)
    np.save(inst_save_path, instrumentals)
    stage1_output_set.append(vocal_save_path)
    stage1_output_set.append(inst_save_path)


    # offload model
    if not disable_offload_model:
        model.cpu()
        del model
        torch.cuda.empty_cache()

    print("Converting to Audio...")

    # convert audio tokens to audio
    def save_audio(wav: torch.Tensor, path, sample_rate: int, rescale: bool = False):
        folder_path = os.path.dirname(path)
        if not os.path.exists(folder_path):
            os.makedirs(folder_path)
        limit = 0.99
        max_val = wav.abs().max()
        wav = wav * min(limit / max_val, 1) if rescale else wav.clamp(-limit, limit)
        torchaudio.save(str(path), wav, sample_rate=sample_rate, encoding='PCM_S', bits_per_sample=16)
    # reconstruct tracks
    recons_output_dir = os.path.join(output_dir, "recons")
    recons_mix_dir = os.path.join(recons_output_dir, 'mix')
    os.makedirs(recons_mix_dir, exist_ok=True)
    tracks = []
    for npy in stage1_output_set:
        codec_result = np.load(npy)
        decodec_rlt=[]
        with torch.no_grad():
            decoded_waveform = codec_model.decode(torch.as_tensor(codec_result.astype(np.int16), dtype=torch.long).unsqueeze(0).permute(1, 0, 2).to(device))
        decoded_waveform = decoded_waveform.cpu().squeeze(0)
        decodec_rlt.append(torch.as_tensor(decoded_waveform))
        decodec_rlt = torch.cat(decodec_rlt, dim=-1)
        save_path = os.path.join(recons_output_dir, os.path.splitext(os.path.basename(npy))[0] + ".mp3")
        tracks.append(save_path)
        save_audio(decodec_rlt, save_path, 16000)
    # mix tracks
    for inst_path in tracks:
        try:
            if (inst_path.endswith('.wav') or inst_path.endswith('.mp3')) \
                and 'instrumental' in inst_path:
                # find pair
                vocal_path = inst_path.replace('instrumental', 'vocal')
                if not os.path.exists(vocal_path):
                    continue
                # mix
                recons_mix = os.path.join(recons_mix_dir, os.path.basename(inst_path).replace('instrumental', 'mixed'))
                vocal_stem, sr = sf.read(inst_path)
                instrumental_stem, _ = sf.read(vocal_path)
                mix_stem = (vocal_stem + instrumental_stem) / 1
                sf.write(recons_mix, mix_stem, sr)
        except Exception as e:
            print(e)

    # vocoder to upsample audios
    vocal_decoder, inst_decoder = build_codec_model(config_path, vocal_decoder_path, inst_decoder_path)
    vocoder_output_dir = os.path.join(output_dir, 'vocoder')
    vocoder_stems_dir = os.path.join(vocoder_output_dir, 'stems')
    vocoder_mix_dir = os.path.join(vocoder_output_dir, 'mix')
    os.makedirs(vocoder_mix_dir, exist_ok=True)
    os.makedirs(vocoder_stems_dir, exist_ok=True)
    instrumental_output = None
    vocal_output = None
    for npy in stage1_output_set:
        if 'instrumental' in npy:
            # Process instrumental
            instrumental_output = process_audio(
                npy,
                os.path.join(vocoder_stems_dir, 'instrumental.mp3'),
                rescale,
                argparse.Namespace(**locals()), # Convert local variables to argparse.Namespace
                inst_decoder,
                codec_model
            )
        else:
            # Process vocal
            vocal_output = process_audio(
                npy,
                os.path.join(vocoder_stems_dir, 'vocal.mp3'),
                rescale,
                 argparse.Namespace(**locals()), # Convert local variables to argparse.Namespace
                vocal_decoder,
                codec_model
            )
    # mix tracks
    try:
        mix_output = instrumental_output + vocal_output
        vocoder_mix = os.path.join(vocoder_mix_dir, os.path.basename(recons_mix))
        save_audio(mix_output, vocoder_mix, 44100, rescale)
        print(f"Created mix: {vocoder_mix}")
        return vocoder_mix
    except RuntimeError as e:
        print(e)
        print(f"mix {vocoder_mix} failed! inst: {instrumental_output.shape}, vocal: {vocal_output.shape}")

    # Post process
    replace_low_freq_with_energy_matched(
        a_file=recons_mix,     # 16kHz
        b_file=vocoder_mix,     # 48kHz
        c_file=os.path.join(output_dir, os.path.basename(recons_mix)),
        cutoff_freq=5500.0
    )
    print("All process Done")


@spaces.GPU(duration=120)
def infer(genre_txt_content, lyrics_txt_content, num_segments=2, max_new_tokens=200):

    # Ensure the output folder exists
    output_dir = "./output"
    os.makedirs(output_dir, exist_ok=True)
    print(f"Output folder ensured at: {output_dir}")

    empty_output_folder(output_dir)
 
    # Command and arguments with optimized settings
    command = [
        "python", "infer.py",
        "--stage1_model", model,
        # "--stage2_model", "m-a-p/YuE-s2-1B-general",
        "--genre_txt", f"{genre_txt_content}",
        "--lyrics_txt", f"{lyrics_txt_content}",
        "--run_n_segments", f"{num_segments}",
        # "--stage2_batch_size", "4",
        "--output_dir", f"{output_dir}",
        "--cuda_idx", "0",
        "--max_new_tokens", f"{max_new_tokens}",
        # "--disable_offload_model"
    ]
    
    # Execute the command
    try:
        music = generate_music(stage1_model=model, genre_txt=genre_txt_content, lyrics_txt=lyrics_txt_content, run_n_segments=num_segments, output_dir=output_dir, cuda_idx=0, max_new_tokens=max_new_tokens)
        
        # Check and print the contents of the output folder
        output_files = os.listdir(output_dir)
        if output_files:
            print("Output folder contents:")
            for file in output_files:
                print(f"- {file}")

            last_mp3 = get_last_mp3_file(output_dir)

            if last_mp3:
                print("Last .mp3 file:", last_mp3)
                return last_mp3
            else:
                return None
        else:
            print("Output folder is empty.")
            return None
    except subprocess.CalledProcessError as e:
        print(f"Error occurred: {e}")
        return None
    finally:
        # Clean up temporary files
        print("Temporary files deleted.")

# Gradio 

with gr.Blocks() as demo:
    with gr.Column():
        gr.Markdown("# YuE: Open Music Foundation Models for Full-Song Generation")
        gr.HTML("""
        <div style="display:flex;column-gap:4px;">
            <a href="https://github.com/multimodal-art-projection/YuE">
                <img src='https://img.shields.io/badge/GitHub-Repo-blue'>
            </a> 
            <a href="https://map-yue.github.io">
                <img src='https://img.shields.io/badge/Project-Page-green'>
            </a>
            <a href="https://huggingface.co/spaces/innova-ai/YuE-music-generator-demo?duplicate=true">
                <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-sm.svg" alt="Duplicate this Space">
            </a>
        </div>
        """)
        with gr.Row():
            with gr.Column():
                genre_txt = gr.Textbox(label="Genre")
                lyrics_txt = gr.Textbox(label="Lyrics")
                
            with gr.Column():
                if is_shared_ui:
                    num_segments = gr.Number(label="Number of Segments", value=2, interactive=True)
                    max_new_tokens = gr.Slider(label="Max New Tokens", minimum=500, maximum="3000", step=500, value=500, interactive=True) # increase it after testing
                else:
                    num_segments = gr.Number(label="Number of Song Segments", value=2, interactive=True)
                    max_new_tokens = gr.Slider(label="Max New Tokens", minimum=500, maximum="24000", step=500, value=3000, interactive=True)
                submit_btn = gr.Button("Submit")
                music_out = gr.Audio(label="Audio Result")

        gr.Examples(
            examples = [
                [
                    "female blues airy vocal bright vocal piano sad romantic guitar jazz",
                    """[verse]
In the quiet of the evening, shadows start to fall
Whispers of the night wind echo through the hall
Lost within the silence, I hear your gentle voice
Guiding me back homeward, making my heart rejoice

[chorus]
Don't let this moment fade, hold me close tonight
With you here beside me, everything's alright
Can't imagine life alone, don't want to let you go
Stay with me forever, let our love just flow
                    """
                ],
                [
                    "rap piano street tough piercing vocal hip-hop synthesizer clear vocal male",
                    """[verse]
Woke up in the morning, sun is shining bright
Chasing all my dreams, gotta get my mind right
City lights are fading, but my vision's clear
Got my team beside me, no room for fear
Walking through the streets, beats inside my head
Every step I take, closer to the bread
People passing by, they don't understand
Building up my future with my own two hands

[chorus]
This is my life, and I'm aiming for the top
Never gonna quit, no, I'm never gonna stop
Through the highs and lows, I'mma keep it real
Living out my dreams with this mic and a deal
                    """
                ]
            ], 
             inputs = [genre_txt, lyrics_txt],
            outputs = [music_out],
            cache_examples = False,
            # cache_mode="lazy",
            fn=infer
        )
    
    submit_btn.click(
        fn = infer, 
        inputs = [genre_txt, lyrics_txt, num_segments, max_new_tokens],
        outputs = [music_out]
    )
demo.queue().launch(show_api=False, show_error=True)