Update app.py

app.py

@@ -46,8 +46,6 @@ 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
@@ -66,8 +64,8 @@ import time
 import copy
 from collections import Counter
 from models.soundstream_hubert_new import SoundStream
-#from vocoder import build_codec_model, process_audio # removed vocoder
-#from post_process_audio import replace_low_freq_with_energy_matched # removed post process
+
+# don't change above code
 
 device = "cuda:0"
 
@@ -81,9 +79,6 @@ model.eval()
 
 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' # removed vocoder
-#vocal_decoder_path = './xcodec_mini_infer/decoders/decoder_131000.pth' # removed vocoder
-#inst_decoder_path = './xcodec_mini_infer/decoders/decoder_151000.pth' # removed vocoder
 
 mmtokenizer = _MMSentencePieceTokenizer("./mm_tokenizer_v0.2_hf/tokenizer.model")
 
@@ -93,18 +88,8 @@ 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 = torch.compile(codec_model)
 codec_model.eval()
 
-# Preload and compile vocoders # removed vocoder
-#vocal_decoder, inst_decoder = build_codec_model(config_path, vocal_decoder_path, inst_decoder_path)
-#vocal_decoder.to(device)
-#inst_decoder.to(device)
-#vocal_decoder = torch.compile(vocal_decoder)
-#inst_decoder = torch.compile(inst_decoder)
-#vocal_decoder.eval()
-#inst_decoder.eval()
-
 @spaces.GPU(duration=120)
 def generate_music(
         max_new_tokens=5,
@@ -117,234 +102,174 @@ def generate_music(
         prompt_end_time=30.0,
         cuda_idx=0,
         rescale=False,
-        batch_size=1
 ):
     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'!")
     cuda_idx = cuda_idx
     max_new_tokens = max_new_tokens * 100
 
-    with tempfile.TemporaryDirectory() as output_dir:
-        stage1_output_dir = os.path.join(output_dir, f"stage1")
-        os.makedirs(stage1_output_dir, exist_ok=True)
-
-        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: str):
-            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 = []
-        vocals_list = []
-        instrumentals_list = []
-        genres = genre_txt.strip()
-        lyrics = split_lyrics(lyrics_txt + "\n")
-        # 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
-
-        # special tokens
-        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))
-        
-        batches = [prompt_texts[i:i + batch_size] for i in range(0, run_n_segments, batch_size)]
-
-        print(batches)
-
-        for batch_idx, batch in enumerate(tqdm(batches)):
-            random_ids = [uuid.uuid4() for _ in range(len(batch))]
-            raw_outputs = [None] * len(batch)
-
-            # Here is suggested decoding config
-            top_p = 0.93
-            temperature = 1.0
-            repetition_penalty = 1.2
-
-            for i, p in enumerate(batch):
-                section_text = p.replace('[start_of_segment]', '').replace('[end_of_segment]', '')
-                # Adjust guidance scale for the first two sections to be lower
-                guidance_scale = 1.5 if (batch_idx*batch_size + i) <= 1 else 1.2
-
-                if (batch_idx*batch_size + i) == 0:
-                    continue  # Skip the first instruction
-
-                if (batch_idx * batch_size + 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_outputs[i], prompt_ids], dim=1) if (batch_idx * batch_size + 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 {(batch_idx * batch_size + 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,
-                        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 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 (batch_idx * batch_size + i) > 1:
-                    raw_outputs[i] = torch.cat([raw_outputs[i], prompt_ids, output_seq[:, input_ids.shape[-1]:]], dim=1)
-                else:
-                    raw_outputs[i] = output_seq
-
-            for i, raw_output in enumerate(raw_outputs):
-                # 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)}')
-                
-                range_begin = 1 if use_audio_prompt and batch_idx == 0 else 0
-
-                vocals_batch = []
-                instrumentals_batch = []
-                for j in range(range_begin, len(soa_idx)):
-                    codec_ids = ids[soa_idx[j] + 1:eoa_idx[j]]
-                    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_batch.append(vocals_ids)
-                    instrumentals_ids = codectool.ids2npy(rearrange(codec_ids, "(n b) -> b n", b=2)[1])
-                    instrumentals_batch.append(instrumentals_ids)
-
-                vocals_batch = np.concatenate(vocals_batch, axis=1)
-                instrumentals_batch = np.concatenate(instrumentals_batch, axis=1)
-                
-                vocals_list.append(vocals_batch)
-                instrumentals_list.append(instrumentals_batch)
-
-        vocals = np.concatenate(vocals_list, axis=1)
-        instrumentals = np.concatenate(instrumentals_list, axis=1)
-
-        vocal_save_path = os.path.join(stage1_output_dir, f"vocal_{random_ids[0]}".replace('.', '@') + '.npy')
-        inst_save_path = os.path.join(stage1_output_dir, f"instrumental_{random_ids[0]}".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 = []
-
-        vocal_stem = None
-        instrumental_stem = None
-        sr = None
-
-        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)
-            if 'vocal' in npy:
-                vocal_stem = decodec_rlt.numpy()
-            elif 'instrumental' in npy:
-                instrumental_stem = decodec_rlt.numpy()
-            sr = 16000
+    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: str):
+        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 = []
+
+    genres = genre_txt.strip()
+    lyrics = split_lyrics(lyrics_txt + "\n")
+    # 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.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,
+                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 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_audio = None
+    instrumental_audio = None
+    mixed_audio = None
+
+    # convert audio tokens to audio
+    def convert_to_audio(codec_result, rescale):
+        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)
         
-        # mix tracks
-        if vocal_stem is not None and instrumental_stem is not None:
-            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))
+        limit = 0.99
+        max_val = decoded_waveform.abs().max()
+        scaled_waveform = decoded_waveform * min(limit / max_val, 1) if rescale else decoded_waveform.clamp(-limit, limit)
+        return (16000, (scaled_waveform * 32767).astype(np.int16))
 
-        else:
-            print("Missing Vocal or Instrumental Stem")
-            return None, None, None
+    vocal_audio = convert_to_audio(vocals, rescale)
+    instrumental_audio = convert_to_audio(instrumentals, rescale)
+
+    mix_stem = (vocal_audio[1] + instrumental_audio[1]) / 1 # mixing by summing and dividing
+    mixed_audio = (vocal_audio[0], mix_stem) # same sample rate
+
+    return mixed_audio, vocal_audio, instrumental_audio
 
 def infer(genre_txt_content, lyrics_txt_content, num_segments=2, max_new_tokens=15):
     # Execute the command
     try:
         mixed_audio_data, vocal_audio_data, instrumental_audio_data = generate_music(genre_txt=genre_txt_content, lyrics_txt=lyrics_txt_content, run_n_segments=num_segments,
-                               cuda_idx=0, max_new_tokens=max_new_tokens, batch_size=4)
+                               cuda_idx=0, max_new_tokens=max_new_tokens)
         return mixed_audio_data, vocal_audio_data, instrumental_audio_data
     except Exception as e:
         gr.Warning("An Error Occured: " + str(e))
@@ -430,7 +355,6 @@ Living out my dreams with this mic and a deal
             inputs=[genre_txt, lyrics_txt],
             outputs=[music_out, vocal_out, instrumental_out],
             cache_examples=True,
-            cache_mode="eager",
             fn=infer
         )