Update app.py

app.py

@@ -1,8 +1,9 @@
 import gradio as gr
 import subprocess
 import os
-import spaces
 import shutil
+import tempfile
+import spaces
 import torch
 import sys
 import uuid
@@ -18,8 +19,10 @@ subprocess.run(
 
 from huggingface_hub import snapshot_download
 
-# Create xcodec_mini_infer folder if it does not exist
+# Create xcodec_mini_infer folder
 folder_path = './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}")
@@ -31,7 +34,7 @@ snapshot_download(
     local_dir="./xcodec_mini_infer"
 )
 
-# Change working directory if needed
+# Change to the "inference" directory
 inference_dir = "."
 try:
     os.chdir(inference_dir)
@@ -43,13 +46,16 @@ except FileNotFoundError:
 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'))
 
+# don't change above code
+
+import argparse
 import numpy as np
 import json
-import argparse
 from omegaconf import OmegaConf
 import torchaudio
 from torchaudio.transforms import Resample
 import soundfile as sf
+
 from tqdm import tqdm
 from einops import rearrange
 from codecmanipulator import CodecManipulator
@@ -61,14 +67,12 @@ import copy
 from collections import Counter
 from models.soundstream_hubert_new import SoundStream
 
-# ---------------------------------------------------------------------
-# Load models, configurations, and tokenizers (run once at startup)
-# ---------------------------------------------------------------------
 device = "cuda:0"
 
+# Load model and tokenizer outside the generation function (load once)
 print("Loading model...")
 model = AutoModelForCausalLM.from_pretrained(
-    "m-a-p/YuE-s1-7B-anneal-en-cot",
+    "m-a-p/YuE-s1-7B-anneal-en-cot",  # "m-a-p/YuE-s1-7B-anneal-en-icl",
     torch_dtype=torch.float16,
     attn_implementation="flash_attention_2",
 ).to(device)
@@ -79,9 +83,9 @@ basic_model_config = './xcodec_mini_infer/final_ckpt/config.yaml'
 resume_path = './xcodec_mini_infer/final_ckpt/ckpt_00360000.pth'
 
 mmtokenizer = _MMSentencePieceTokenizer("./mm_tokenizer_v0.2_hf/tokenizer.model")
+
 codectool = CodecManipulator("xcodec", 0, 1)
 model_config = OmegaConf.load(basic_model_config)
-
 # Load codec model
 codec_model = eval(model_config.generator.name)(**model_config.generator.config).to(device)
 parameter_dict = torch.load(resume_path, map_location='cpu')
@@ -89,9 +93,7 @@ codec_model.load_state_dict(parameter_dict['codec_model'])
 codec_model.eval()
 print("Codec model loaded.")
 
-# ---------------------------------------------------------------------
-# Helper Classes and Functions
-# ---------------------------------------------------------------------
+
 class BlockTokenRangeProcessor(LogitsProcessor):
     def __init__(self, start_id, end_id):
         self.blocked_token_ids = list(range(start_id, end_id))
@@ -116,69 +118,12 @@ def split_lyrics(lyrics: str):
     structured_lyrics = [f"[{seg[0]}]\n{seg[1].strip()}\n\n" for seg in segments]
     return structured_lyrics
 
-# ---------------------------
-# CUDA Heavy Functions
-# ---------------------------
-def requires_cuda_generation(input_ids, max_new_tokens, top_p, temperature, repetition_penalty, guidance_scale):
-    """
-    Performs the CUDA-intensive generation using the language model.
-    """
-    with torch.inference_mode(), torch.autocast(device_type='cuda', dtype=torch.float16):
-        output_seq = model.generate(
-            input_ids=input_ids,
-            max_new_tokens=max_new_tokens,
-            min_new_tokens=100,  # To avoid too-short generations
-            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,
-            use_cache=True
-        )
-        # If the generated sequence does not end with the end-of-audio token, append it.
-        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)
-    return output_seq
-
-def requires_cuda_decode(codec_result):
-    """
-    Uses the codec model on the GPU to decode a given numpy array of codec IDs
-    into a waveform tensor.
-    """
-    with torch.no_grad():
-        # Convert the numpy result to tensor and move to device
-        codec_tensor = torch.as_tensor(codec_result.astype(np.int16), dtype=torch.long)
-        # The expected shape is (seq_len, batch, channels), so we add and permute dims as needed.
-        codec_tensor = codec_tensor.unsqueeze(0).permute(1, 0, 2).to(device)
-        decoded_waveform = codec_model.decode(codec_tensor)
-    return decoded_waveform.cpu().squeeze(0)
-
-def save_audio(wav: torch.Tensor, sample_rate: int, rescale: bool = False):
-    """
-    Convert a waveform tensor to a numpy array (16-bit PCM) without writing to disk.
-    """
-    limit = 0.99
-    max_val = wav.abs().max()
-    wav = wav * min(limit / max_val, 1) if rescale else wav.clamp(-limit, limit)
-    # Return a tuple as expected by Gradio: (sample_rate, np.array)
-    return sample_rate, (wav.numpy() * 32767).astype(np.int16)
-
-# ---------------------------------------------------------------------
-# Main Generation Function (without temporary files/directories)
-# ---------------------------------------------------------------------
 @spaces.GPU(duration=175)
 def generate_music(
         genre_txt=None,
         lyrics_txt=None,
         run_n_segments=2,
-        max_new_tokens=23,
+        max_new_tokens=15,
         use_audio_prompt=False,
         audio_prompt_path="",
         prompt_start_time=0.0,
@@ -187,147 +132,185 @@ def generate_music(
         rescale=False,
 ):
     """
-    Generates music based on genre and lyrics (and optionally an audio prompt).
-    The heavy CUDA computations are performed in helper functions.
-    All intermediate data is kept in memory.
+    Generates music based on given genre and lyrics, optionally using an audio prompt.
+    This function orchestrates the music generation process, including prompt formatting,
+    model inference, and audio post-processing.
     """
     if use_audio_prompt and not audio_prompt_path:
         raise FileNotFoundError("Please provide an audio prompt file when 'Use Audio Prompt' is enabled!")
-
-    # Scale max_new_tokens (e.g. each token may correspond to 100 time units)
+    cuda_idx = cuda_idx
     max_new_tokens = max_new_tokens * 100
 
-    # Prepare prompt texts from genre and lyrics
-    genres = genre_txt.strip()
-    lyrics_segments = split_lyrics(lyrics_txt + "\n")
-    full_lyrics = "\n".join(lyrics_segments)
-    # The first prompt is the overall instruction and full lyrics.
-    prompt_texts = [f"Generate music from the given lyrics segment by segment.\n[Genre] {genres}\n{full_lyrics}"]
-    # Then add each individual lyric segment.
-    prompt_texts += lyrics_segments
-
-    random_id = uuid.uuid4()
-    raw_output = None
-
-    # Generation configuration
-    top_p = 0.93
-    temperature = 1.0
-    repetition_penalty = 1.2
-    start_of_segment = mmtokenizer.tokenize('[start_of_segment]')
-    end_of_segment = mmtokenizer.tokenize('[end_of_segment]')
-
-    # Limit the number of segments to generate (adding 1 because the first prompt is a header)
-    run_n_segments = min(run_n_segments + 1, len(prompt_texts))
-
-    print("Starting generation for segments:")
-    print(list(enumerate(tqdm(prompt_texts[:run_n_segments]))))
-
-    # Loop over each prompt segment
-    for i, p in enumerate(tqdm(prompt_texts[:run_n_segments])):
-        section_text = p.replace('[start_of_segment]', '').replace('[end_of_segment]', '')
-        # Adjust guidance scale based on segment index
-        guidance_scale = 1.5 if i <= 1 else 1.2
-
-        # For the header prompt, we just use the tokenized text.
-        if i == 0:
-            continue
-
-        if i == 1:
-            # Process audio prompt if provided
-            if use_audio_prompt:
-                audio_prompt = load_audio_mono(audio_prompt_path)
-                audio_prompt = 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)
-                code_ids = codectool.npy2ids(raw_codes[0])
-                # Select a slice corresponding to the provided time range.
-                audio_prompt_codec = code_ids[int(prompt_start_time * 50): int(prompt_end_time * 50)]
-                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
+    with tempfile.TemporaryDirectory() as output_dir:
+        stage1_output_dir = os.path.join(output_dir, f"stage1")
+        os.makedirs(stage1_output_dir, exist_ok=True)
+
+        stage1_output_set = []
+
+        genres = genre_txt.strip()
+        lyrics = split_lyrics(lyrics_txt + "\n")
+        # instruction
+        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()
+        raw_output = None
+
+        # Decoding config
+        top_p = 0.93
+        temperature = 1.0
+        repetition_penalty = 1.2
+        start_of_segment = mmtokenizer.tokenize('[start_of_segment]')
+        end_of_segment = mmtokenizer.tokenize('[end_of_segment]')
+
+        # 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  # Guidance scale adjusted based on segment index
+            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:
-                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
-
-        # Convert prompt tokens to tensor and move to device
-        prompt_ids = torch.as_tensor(prompt_ids).unsqueeze(0).to(device)
-        input_ids = torch.cat([raw_output, prompt_ids], dim=1) if (i > 1 and raw_output is not None) else prompt_ids
-
-        # Ensure input length does not exceed model context window (using last tokens if needed)
-        max_context = 16384 - max_new_tokens - 1
-        if input_ids.shape[-1] > max_context:
-            print(
-                f'Section {i}: input length {input_ids.shape[-1]} exceeds context length {max_context}. Using last {max_context} tokens.'
-            )
-            input_ids = input_ids[:, -max_context:]
-
-        # Generate new tokens using the CUDA-heavy helper function
-        output_seq = requires_cuda_generation(
-            input_ids,
-            max_new_tokens,
-            top_p,
-            temperature,
-            repetition_penalty,
-            guidance_scale
-        )
+                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.inference_mode(), torch.autocast(device_type='cuda', dtype=torch.float16):
+                output_seq = model.generate(
+                    input_ids=input_ids,
+                    max_new_tokens=max_new_tokens,
+                    min_new_tokens=100,  # Keep min_new_tokens to avoid short generations
+                    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,
+                    use_cache=True,
+                    num_beams=2
+                )
+                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)
+
+            output_seq = model_inference(input_ids, max_new_tokens, top_p, temperature, repetition_penalty, guidance_scale)
+
+            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 * (len(codec_ids) // 2)]  # Ensure even length for reshape
+            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"vocal_{random_id}".replace('.', '@') + '.npy')
+        inst_save_path = os.path.join(stage1_output_dir, f"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)
+
+        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")  # Save as mp3 for gradio
+            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(vocal_path)
+                    instrumental_stem, _ = sf.read(inst_path)
+                    mix_stem = (vocal_stem + instrumental_stem) / 1
+                    return (sr, (mix_stem * 32767).astype(np.int16)), (sr, (vocal_stem * 32767).astype(np.int16)), (sr, (instrumental_stem * 32767).astype(np.int16))
+            except Exception as e:
+                print(e)
+                return None, None, None
 
-        # Accumulate outputs across segments
-        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(f"Accumulated output length: {raw_output.shape[-1]} tokens")
-
-    # After generation, convert raw output tokens into codec IDs.
-    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_list = []
-    instrumentals_list = []
-    # If an audio prompt was used, skip the first pair.
-    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:]
-        # Ensure even length for reshaping into two tracks (vocal and instrumental)
-        codec_ids = codec_ids[:2 * (len(codec_ids) // 2)]
-        reshaped = rearrange(codec_ids, "(n b) -> b n", b=2)
-        vocals_ids = codectool.ids2npy(reshaped[0])
-        instrumentals_ids = codectool.ids2npy(reshaped[1])
-        vocals_list.append(vocals_ids)
-        instrumentals_list.append(instrumentals_ids)
-
-    # Concatenate segments in time dimension
-    vocals_codec = np.concatenate(vocals_list, axis=1)
-    instrumentals_codec = np.concatenate(instrumentals_list, axis=1)
-
-    print("Decoding audio on GPU...")
-
-    # Decode the codec arrays to waveforms using the CUDA helper function.
-    vocal_waveform = requires_cuda_decode(vocals_codec)
-    instrumental_waveform = requires_cuda_decode(instrumentals_codec)
-
-    # Mix the two waveforms (simple summation)
-    mixed_waveform = (vocal_waveform + instrumental_waveform) / 1.0
-
-    # Return the three audio outputs (mixed, vocal, instrumental) as tuples (sample_rate, np.array)
-    sample_rate = 16000
-    mixed_audio = save_audio(mixed_waveform, sample_rate, rescale)
-    vocal_audio = save_audio(vocal_waveform, sample_rate, rescale)
-    instrumental_audio = save_audio(instrumental_waveform, sample_rate, rescale)
-    return mixed_audio, vocal_audio, instrumental_audio
-
-# ---------------------------------------------------------------------
 # Gradio Interface
-# ---------------------------------------------------------------------
 with gr.Blocks() as demo:
     with gr.Column():
         gr.Markdown("# YuE: Open Music Foundation Models for Full-Song Generation")
@@ -360,6 +343,7 @@ with gr.Blocks() as demo:
                     instrumental_out = gr.Audio(label="Instrumental Audio")
         gr.Markdown("## Call for Contributions\nIf you find this space interesting please feel free to contribute.")
 
+        # When the "Submit" button is clicked, pass the additional audio-related inputs to the function.
         submit_btn.click(
             fn=generate_music,
             inputs=[
@@ -373,6 +357,7 @@ with gr.Blocks() as demo:
             outputs=[music_out, vocal_out, instrumental_out]
         )
 
+        # Examples updated to only include text inputs
         gr.Examples(
             examples=[
                 [
@@ -419,4 +404,4 @@ Locked inside my mind, hot flame.
             fn=generate_music
         )
 
-demo.queue().launch(show_error=True)
+demo.queue().launch(show_error=True)