import os import lightning as L import torch import glob import time from snac import SNAC from litgpt import Tokenizer from litgpt.utils import ( num_parameters, ) from litgpt.generate.base import ( generate_AA, generate_ASR, generate_TA, generate_TT, generate_AT, generate_TA_BATCH, next_token_image_batch ) import soundfile as sf from litgpt.model import GPT, Config from lightning.fabric.utilities.load import _lazy_load as lazy_load from utils.snac_utils import layershift, reconscruct_snac, reconstruct_tensors, get_time_str from utils.snac_utils import get_snac, generate_audio_data import whisper from tqdm import tqdm from huggingface_hub import snapshot_download torch.set_printoptions(sci_mode=False) # TODO text_vocabsize = 151936 text_specialtokens = 64 audio_vocabsize = 4096 audio_specialtokens = 64 padded_text_vocabsize = text_vocabsize + text_specialtokens padded_audio_vocabsize = audio_vocabsize + audio_specialtokens _eot = text_vocabsize _pad_t = text_vocabsize + 1 _input_t = text_vocabsize + 2 _answer_t = text_vocabsize + 3 _asr = text_vocabsize + 4 _eoa = audio_vocabsize _pad_a = audio_vocabsize + 1 _input_a = audio_vocabsize + 2 _answer_a = audio_vocabsize + 3 _split = audio_vocabsize + 4 _image = audio_vocabsize + 5 _eoimage = audio_vocabsize + 6 def get_input_ids_TA(text, text_tokenizer): input_ids_item = [[] for _ in range(8)] text_tokens = text_tokenizer.encode(text) for i in range(7): input_ids_item[i] = [layershift(_pad_a, i)] * (len(text_tokens) + 2) + [ layershift(_answer_a, i) ] input_ids_item[i] = torch.tensor(input_ids_item[i]).unsqueeze(0) input_ids_item[-1] = [_input_t] + text_tokens.tolist() + [_eot] + [_answer_t] input_ids_item[-1] = torch.tensor(input_ids_item[-1]).unsqueeze(0) return input_ids_item def get_input_ids_TT(text, text_tokenizer): input_ids_item = [[] for i in range(8)] text_tokens = text_tokenizer.encode(text).tolist() for i in range(7): input_ids_item[i] = torch.tensor( [layershift(_pad_a, i)] * (len(text_tokens) + 3) ).unsqueeze(0) input_ids_item[-1] = [_input_t] + text_tokens + [_eot] + [_answer_t] input_ids_item[-1] = torch.tensor(input_ids_item[-1]).unsqueeze(0) return input_ids_item def get_input_ids_whisper( mel, leng, whispermodel, device, special_token_a=_answer_a, special_token_t=_answer_t, ): with torch.no_grad(): mel = mel.unsqueeze(0).to(device) # audio_feature = whisper.decode(whispermodel,mel, options).audio_features audio_feature = whispermodel.embed_audio(mel)[0][:leng] T = audio_feature.size(0) input_ids = [] for i in range(7): input_ids_item = [] input_ids_item.append(layershift(_input_a, i)) input_ids_item += [layershift(_pad_a, i)] * T input_ids_item += [(layershift(_eoa, i)), layershift(special_token_a, i)] input_ids.append(torch.tensor(input_ids_item).unsqueeze(0)) input_id_T = torch.tensor([_input_t] + [_pad_t] * T + [_eot, special_token_t]) input_ids.append(input_id_T.unsqueeze(0)) return audio_feature.unsqueeze(0), input_ids def get_input_ids_whisper_ATBatch(mel, leng, whispermodel, device): with torch.no_grad(): mel = mel.unsqueeze(0).to(device) # audio_feature = whisper.decode(whispermodel,mel, options).audio_features audio_feature = whispermodel.embed_audio(mel)[0][:leng] T = audio_feature.size(0) input_ids_AA = [] for i in range(7): input_ids_item = [] input_ids_item.append(layershift(_input_a, i)) input_ids_item += [layershift(_pad_a, i)] * T input_ids_item += [(layershift(_eoa, i)), layershift(_answer_a, i)] input_ids_AA.append(torch.tensor(input_ids_item)) input_id_T = torch.tensor([_input_t] + [_pad_t] * T + [_eot, _answer_t]) input_ids_AA.append(input_id_T) input_ids_AT = [] for i in range(7): input_ids_item = [] input_ids_item.append(layershift(_input_a, i)) input_ids_item += [layershift(_pad_a, i)] * T input_ids_item += [(layershift(_eoa, i)), layershift(_pad_a, i)] input_ids_AT.append(torch.tensor(input_ids_item)) input_id_T = torch.tensor([_input_t] + [_pad_t] * T + [_eot, _answer_t]) input_ids_AT.append(input_id_T) input_ids = [input_ids_AA, input_ids_AT] stacked_inputids = [[] for _ in range(8)] for i in range(2): for j in range(8): stacked_inputids[j].append(input_ids[i][j]) stacked_inputids = [torch.stack(tensors) for tensors in stacked_inputids] return torch.stack([audio_feature, audio_feature]), stacked_inputids def load_audio(path): audio = whisper.load_audio(path) duration_ms = (len(audio) / 16000) * 1000 audio = whisper.pad_or_trim(audio) mel = whisper.log_mel_spectrogram(audio) return mel, int(duration_ms / 20) + 1 def A1_A2_batch(fabric, audio_feature, input_ids, leng, model, text_tokenizer, step, snacmodel, out_dir=None): with fabric.init_tensor(): model.set_kv_cache(batch_size=2) tokenlist = generate_TA_BATCH( model, audio_feature, input_ids, [leng, leng], ["A1A2", "A1T2"], max_returned_tokens=2048, temperature=0.9, top_k=1, eos_id_a=_eoa, eos_id_t=_eot, pad_id_t=_pad_t, shift=padded_text_vocabsize, include_prompt=True, generate_text=True, ) text_tokenlist = tokenlist[-1] if text_vocabsize in text_tokenlist: text_tokenlist = text_tokenlist[: text_tokenlist.index(text_vocabsize)] text = text_tokenizer.decode(torch.tensor(text_tokenlist)).strip() audio_tokenlist = tokenlist[:-1] audiolist = reconscruct_snac(audio_tokenlist) audio = reconstruct_tensors(audiolist) if out_dir is None: out_dir = "./output/default/A1-A2-batch" else: out_dir = out_dir + "/A1-A2-batch" if not os.path.exists(out_dir): os.makedirs(out_dir) with torch.inference_mode(): audio_hat = snacmodel.decode(audio) sf.write( f"{out_dir}/{step:02d}.wav", audio_hat.squeeze().cpu().numpy(), 24000, ) model.clear_kv_cache() return text def A1_T2(fabric, audio_feature, input_ids, leng, model, text_tokenizer, step): with fabric.init_tensor(): model.set_kv_cache(batch_size=1) tokenlist = generate_AT( model, audio_feature, input_ids, [leng], ["AT"], max_returned_tokens=2048, temperature=0.9, top_k=1, eos_id_a=_eoa, eos_id_t=_eot, pad_id_t=_pad_t, shift=padded_text_vocabsize, include_prompt=True, generate_text=True, ) return text_tokenizer.decode(torch.tensor(tokenlist)).strip() def A1_A2(fabric, audio_feature, input_ids, leng, model, text_tokenizer, step, snacmodel, out_dir=None): with fabric.init_tensor(): model.set_kv_cache(batch_size=1) tokenlist = generate_AA( model, audio_feature, input_ids, [leng], ["A1T2"], max_returned_tokens=2048, temperature=0.9, top_k=1, eos_id_a=_eoa, eos_id_t=_eot, pad_id_t=_pad_t, shift=padded_text_vocabsize, include_prompt=True, generate_text=True, ) audiolist = reconscruct_snac(tokenlist) tokenlist = tokenlist[-1] if text_vocabsize in tokenlist: tokenlist = tokenlist[: tokenlist.index(text_vocabsize)] if out_dir is None: out_dir = "./output/default/A1-A2" else: out_dir = out_dir + "/A1-A2" if not os.path.exists(out_dir): os.makedirs(out_dir) audio = reconstruct_tensors(audiolist) with torch.inference_mode(): audio_hat = snacmodel.decode(audio) sf.write( f"{out_dir}/{step:02d}.wav", audio_hat.squeeze().cpu().numpy(), 24000, ) model.clear_kv_cache() return text_tokenizer.decode(torch.tensor(tokenlist)).strip() def A1_T1(fabric, audio_feature, input_ids, leng, model, text_tokenizer, step): with fabric.init_tensor(): model.set_kv_cache(batch_size=1) tokenlist = generate_ASR( model, audio_feature, input_ids, [leng], ["A1T1"], max_returned_tokens=2048, temperature=0.9, top_k=1, eos_id_a=_eoa, eos_id_t=_eot, pad_id_t=_pad_t, shift=padded_text_vocabsize, include_prompt=True, generate_text=True, ) model.clear_kv_cache() return text_tokenizer.decode(torch.tensor(tokenlist)).strip() def T1_A2(fabric, input_ids, model, text_tokenizer, step, snacmodel, out_dir=None): with fabric.init_tensor(): model.set_kv_cache(batch_size=1) tokenlist = generate_TA( model, None, input_ids, None, ["T1A2"], max_returned_tokens=2048, temperature=0.9, top_k=1, eos_id_a=_eoa, eos_id_t=_eot, pad_id_t=_pad_t, shift=padded_text_vocabsize, include_prompt=True, generate_text=True, ) audiolist = reconscruct_snac(tokenlist) tokenlist = tokenlist[-1] if text_vocabsize in tokenlist: tokenlist = tokenlist[: tokenlist.index(text_vocabsize)] audio = reconstruct_tensors(audiolist) if out_dir is None: out_dir = "./output/default/T1-A2" else: out_dir = out_dir + "/T1-A2" if not os.path.exists(out_dir): os.makedirs(out_dir) with torch.inference_mode(): audio_hat = snacmodel.decode(audio) sf.write( f"{out_dir}/{step:02d}.wav", audio_hat.squeeze().cpu().numpy(), 24000, ) model.clear_kv_cache() return text_tokenizer.decode(torch.tensor(tokenlist)).strip() def T1_T2(fabric, input_ids, model, text_tokenizer, step): with fabric.init_tensor(): model.set_kv_cache(batch_size=1) tokenlist = generate_TT( model, None, input_ids, None, ["T1T2"], max_returned_tokens=2048, temperature=0.9, top_k=1, eos_id_a=_eoa, eos_id_t=_eot, pad_id_t=_pad_t, shift=padded_text_vocabsize, include_prompt=True, generate_text=True, ) model.clear_kv_cache() return text_tokenizer.decode(torch.tensor(tokenlist)).strip() def load_model(ckpt_dir, device): snacmodel = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().to(device) whisper_model_path = ckpt_dir + "/small.pt" if not os.path.exists(whisper_model_path): whisper_model_path = "small" whispermodel = whisper.load_model(whisper_model_path).to(device) text_tokenizer = Tokenizer(ckpt_dir) fabric = L.Fabric(devices=1, strategy="auto") config = Config.from_file(ckpt_dir + "/model_config.yaml") config.post_adapter = False with fabric.init_module(empty_init=False): model = GPT(config) model = fabric.setup(model) state_dict = lazy_load(ckpt_dir + "/lit_model.pth") model.load_state_dict(state_dict, strict=True) model.to(device).eval() return fabric, model, text_tokenizer, snacmodel, whispermodel def download_model(ckpt_dir): repo_id = "gpt-omni/mini-omni2" snapshot_download(repo_id, local_dir=ckpt_dir, revision="main") def get_text_stream(list_output, index, text_tokenizer): text_tokens = list_output[-1][index:] index += len(text_tokens) is_text_end = False if text_vocabsize in text_tokens: text_tokens = text_tokens[:text_tokens.index(text_vocabsize)] is_text_end = True if len(text_tokens) == 0: return "", index, is_text_end res_text = text_tokenizer.decode(torch.tensor(text_tokens)) return res_text, index, is_text_end class OmniInference: def __init__(self, ckpt_dir='./checkpoint', device='cuda:0'): self.device = device if not os.path.exists(ckpt_dir): print(f"checkpoint directory {ckpt_dir} not found, downloading from huggingface") download_model(ckpt_dir) self.fabric, self.model, self.text_tokenizer, self.snacmodel, self.whispermodel = load_model(ckpt_dir, device) def warm_up(self, sample='./data/samples/output1.wav'): for _ in self.run_AT_batch_stream(sample): pass @torch.inference_mode() def run_AT_batch_stream(self, audio_path, stream_stride=4, max_returned_tokens=2048, temperature=0.9, top_k=1, top_p=1.0, eos_id_a=_eoa, eos_id_t=_eot, save_path=None ): assert os.path.exists(audio_path), f"audio file {audio_path} not found" model = self.model with self.fabric.init_tensor(): model.set_kv_cache(batch_size=2,device=self.device) mel, leng = load_audio(audio_path) audio_feature, input_ids = get_input_ids_whisper_ATBatch(mel, leng, self.whispermodel, self.device) T = input_ids[0].size(1) device = input_ids[0].device assert max_returned_tokens > T, f"max_returned_tokens {max_returned_tokens} should be greater than audio length {T}" if model.max_seq_length < max_returned_tokens - 1: raise NotImplementedError( f"max_seq_length {model.max_seq_length} needs to be >= {max_returned_tokens - 1}" ) input_pos = torch.tensor([T], device=device) list_output = [[] for i in range(8)] tokens_A, token_T = next_token_image_batch( model, audio_feature.to(torch.float32).to(model.device), None, input_ids, [T - 3, T - 3], ["A1T2", "A1T2"], input_pos=torch.arange(0, T, device=device), temperature=temperature, top_k=top_k, top_p=top_p, ) for i in range(7): list_output[i].append(tokens_A[i].tolist()[0]) list_output[7].append(token_T.tolist()[0]) model_input_ids = [[] for i in range(8)] for i in range(7): tokens_A[i] = tokens_A[i].clone() + padded_text_vocabsize + i * padded_audio_vocabsize model_input_ids[i].append(tokens_A[i].clone().to(device).to(torch.int32)) model_input_ids[i].append(torch.tensor([layershift(4097, i)], device=device)) model_input_ids[i] = torch.stack(model_input_ids[i]) model_input_ids[-1].append(token_T.clone().to(torch.int32)) model_input_ids[-1].append(token_T.clone().to(torch.int32)) model_input_ids[-1] = torch.stack(model_input_ids[-1]) text_end = False index = 1 nums_generate = stream_stride begin_generate = False current_index = 0 text_index = 0 is_text_end = False for _ in tqdm(range(2, max_returned_tokens - T + 1)): tokens_A, token_T = next_token_image_batch( model, None, None, model_input_ids, None, None, input_pos=input_pos, temperature=temperature, top_k=top_k, top_p=top_p, ) if text_end: token_T = torch.tensor([_pad_t], device=device) if tokens_A[-1] == eos_id_a: break if token_T == eos_id_t: text_end = True for i in range(7): list_output[i].append(tokens_A[i].tolist()[0]) list_output[7].append(token_T.tolist()[0]) model_input_ids = [[] for i in range(8)] for i in range(7): tokens_A[i] = tokens_A[i].clone() +padded_text_vocabsize + i * padded_audio_vocabsize model_input_ids[i].append(tokens_A[i].clone().to(device).to(torch.int32)) model_input_ids[i].append( torch.tensor([layershift(4097, i)], device=device) ) model_input_ids[i] = torch.stack(model_input_ids[i]) model_input_ids[-1].append(token_T.clone().to(torch.int32)) model_input_ids[-1].append(token_T.clone().to(torch.int32)) model_input_ids[-1] = torch.stack(model_input_ids[-1]) if index == 7: begin_generate = True if begin_generate: current_index += 1 if current_index == nums_generate: current_index = 0 snac = get_snac(list_output, index, nums_generate) audio_stream = generate_audio_data(snac, self.snacmodel, self.device) if is_text_end: text_stream = "" else: text_stream, text_index, is_text_end = get_text_stream(list_output, text_index, self.text_tokenizer) yield (audio_stream, text_stream) input_pos = input_pos.add_(1) index += 1 text = self.text_tokenizer.decode(torch.tensor(list_output[-1])) print(f"text output: {text}") if save_path is not None: audiolist = reconscruct_snac(list_output) audio = reconstruct_tensors(audiolist) with torch.inference_mode(): audio_hat = self.snacmodel.decode(audio) sf.write(save_path, audio_hat.squeeze().cpu().numpy(), 24000) model.clear_kv_cache() return list_output def test_infer(): device = "cuda:0" out_dir = f"./output/{get_time_str()}" ckpt_dir = f"./checkpoint" if not os.path.exists(ckpt_dir): print(f"checkpoint directory {ckpt_dir} not found, downloading from huggingface") download_model(ckpt_dir) fabric, model, text_tokenizer, snacmodel, whispermodel = load_model(ckpt_dir, device) task = ['A1A2', 'asr', "T1A2", "AA-BATCH", 'T1T2', 'AT'] # prepare test data # TODO test_audio_list = sorted(glob.glob('./data/samples/output*.wav')) test_audio_transcripts = [ "What is your name?", "what are your hobbies?", "Do you like beijing", "How are you feeling today?", "what is the weather like today?", ] test_text_list = [ "What is your name?", "How are you feeling today?", "Can you describe your surroundings?", "What did you do yesterday?", "What is your favorite book and why?", "How do you make a cup of tea?", "What is the weather like today?", "Can you explain the concept of time?", "Can you tell me a joke?", ] # LOAD MODEL with torch.no_grad(): if "A1A2" in task: print("===============================================================") print(" testing A1A2") print("===============================================================") step = 0 for path in test_audio_list: try: mel, leng = load_audio(path) audio_feature, input_ids = get_input_ids_whisper(mel, leng, whispermodel, device) text = A1_A2( fabric, audio_feature, input_ids, leng, model, text_tokenizer, step, snacmodel, out_dir=out_dir, ) print(f"input: {test_audio_transcripts[step]}") print(f"output: {text}") step += 1 print( "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" ) except: print(f"[error] failed to process {path}") print("===============================================================") if 'asr' in task: print("===============================================================") print(" testing asr") print("===============================================================") index = 0 step = 0 for path in test_audio_list: mel, leng = load_audio(path) audio_feature, input_ids = get_input_ids_whisper(mel, leng, whispermodel, device, special_token_a=_pad_a, special_token_t=_asr) output = A1_T1(fabric, audio_feature, input_ids ,leng, model, text_tokenizer, index).lower().replace(',','').replace('.','').replace('?','') print(f"audio_path: {path}") print(f"audio transcript: {test_audio_transcripts[index]}") print(f"asr output: {output}") print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") index += 1 if "T1A2" in task: step = 0 print("\n") print("===============================================================") print(" testing T1A2") print("===============================================================") for text in test_text_list: input_ids = get_input_ids_TA(text, text_tokenizer) text_output = T1_A2(fabric, input_ids, model, text_tokenizer, step, snacmodel, out_dir=out_dir) print(f"input: {text}") print(f"output: {text_output}") print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") step += 1 print("===============================================================") if "T1T2" in task: step = 0 print("\n") print("===============================================================") print(" testing T1T2") print("===============================================================") for text in test_text_list: input_ids = get_input_ids_TT(text, text_tokenizer) text_output = T1_T2(fabric, input_ids, model, text_tokenizer, step) print(f" Input: {text}") print(f"Output: {text_output}") print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") print("===============================================================") if "AT" in task: print("===============================================================") print(" testing A1T2") print("===============================================================") step = 0 for path in test_audio_list: mel, leng = load_audio(path) audio_feature, input_ids = get_input_ids_whisper( mel, leng, whispermodel, device, special_token_a=_pad_a, special_token_t=_answer_t ) text = A1_T2( fabric, audio_feature, input_ids, leng, model, text_tokenizer, step ) print(f"input: {test_audio_transcripts[step]}") print(f"output: {text}") step += 1 print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") print("===============================================================") if "AA-BATCH" in task: print("===============================================================") print(" testing A1A2-BATCH") print("===============================================================") step = 0 for path in test_audio_list: mel, leng = load_audio(path) audio_feature, input_ids = get_input_ids_whisper_ATBatch(mel, leng, whispermodel, device) text = A1_A2_batch( fabric, audio_feature, input_ids, leng, model, text_tokenizer, step, snacmodel, out_dir=out_dir ) print(f"input: {test_audio_transcripts[step]}") print(f"output: {text}") step += 1 print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") print("===============================================================") print("*********************** test end *****************************") if __name__ == "__main__": test_infer()