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import os
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import lightning as L
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import torch
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import glob
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import time
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from snac import SNAC
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from litgpt import Tokenizer
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from litgpt.utils import (
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num_parameters,
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)
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from litgpt.generate.base import (
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generate_AA,
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generate_ASR,
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generate_TA,
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generate_TT,
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generate_AT,
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generate_TA_BATCH,
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next_token_image_batch
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)
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import soundfile as sf
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from litgpt.model import GPT, Config
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from lightning.fabric.utilities.load import _lazy_load as lazy_load
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from utils.snac_utils import layershift, reconscruct_snac, reconstruct_tensors, get_time_str
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from utils.snac_utils import get_snac, generate_audio_data
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import whisper
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from tqdm import tqdm
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from huggingface_hub import snapshot_download
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torch.set_printoptions(sci_mode=False)
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text_vocabsize = 151936
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text_specialtokens = 64
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audio_vocabsize = 4096
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audio_specialtokens = 64
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padded_text_vocabsize = text_vocabsize + text_specialtokens
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padded_audio_vocabsize = audio_vocabsize + audio_specialtokens
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_eot = text_vocabsize
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_pad_t = text_vocabsize + 1
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_input_t = text_vocabsize + 2
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_answer_t = text_vocabsize + 3
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_asr = text_vocabsize + 4
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_eoa = audio_vocabsize
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_pad_a = audio_vocabsize + 1
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_input_a = audio_vocabsize + 2
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_answer_a = audio_vocabsize + 3
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_split = audio_vocabsize + 4
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_image = audio_vocabsize + 5
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_eoimage = audio_vocabsize + 6
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def get_input_ids_TA(text, text_tokenizer):
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input_ids_item = [[] for _ in range(8)]
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text_tokens = text_tokenizer.encode(text)
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for i in range(7):
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input_ids_item[i] = [layershift(_pad_a, i)] * (len(text_tokens) + 2) + [
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layershift(_answer_a, i)
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]
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input_ids_item[i] = torch.tensor(input_ids_item[i]).unsqueeze(0)
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input_ids_item[-1] = [_input_t] + text_tokens.tolist() + [_eot] + [_answer_t]
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input_ids_item[-1] = torch.tensor(input_ids_item[-1]).unsqueeze(0)
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return input_ids_item
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def get_input_ids_TT(text, text_tokenizer):
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input_ids_item = [[] for i in range(8)]
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text_tokens = text_tokenizer.encode(text).tolist()
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for i in range(7):
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input_ids_item[i] = torch.tensor(
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[layershift(_pad_a, i)] * (len(text_tokens) + 3)
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).unsqueeze(0)
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input_ids_item[-1] = [_input_t] + text_tokens + [_eot] + [_answer_t]
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input_ids_item[-1] = torch.tensor(input_ids_item[-1]).unsqueeze(0)
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return input_ids_item
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def get_input_ids_whisper(
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mel, leng, whispermodel, device,
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special_token_a=_answer_a, special_token_t=_answer_t,
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):
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with torch.no_grad():
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mel = mel.unsqueeze(0).to(device)
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audio_feature = whispermodel.embed_audio(mel)[0][:leng]
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T = audio_feature.size(0)
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input_ids = []
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for i in range(7):
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input_ids_item = []
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input_ids_item.append(layershift(_input_a, i))
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input_ids_item += [layershift(_pad_a, i)] * T
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input_ids_item += [(layershift(_eoa, i)), layershift(special_token_a, i)]
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input_ids.append(torch.tensor(input_ids_item).unsqueeze(0))
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input_id_T = torch.tensor([_input_t] + [_pad_t] * T + [_eot, special_token_t])
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input_ids.append(input_id_T.unsqueeze(0))
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return audio_feature.unsqueeze(0), input_ids
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def get_input_ids_whisper_ATBatch(mel, leng, whispermodel, device):
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with torch.no_grad():
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mel = mel.unsqueeze(0).to(device)
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audio_feature = whispermodel.embed_audio(mel)[0][:leng]
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T = audio_feature.size(0)
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input_ids_AA = []
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for i in range(7):
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input_ids_item = []
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input_ids_item.append(layershift(_input_a, i))
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input_ids_item += [layershift(_pad_a, i)] * T
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input_ids_item += [(layershift(_eoa, i)), layershift(_answer_a, i)]
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input_ids_AA.append(torch.tensor(input_ids_item))
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input_id_T = torch.tensor([_input_t] + [_pad_t] * T + [_eot, _answer_t])
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input_ids_AA.append(input_id_T)
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input_ids_AT = []
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for i in range(7):
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input_ids_item = []
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input_ids_item.append(layershift(_input_a, i))
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input_ids_item += [layershift(_pad_a, i)] * T
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input_ids_item += [(layershift(_eoa, i)), layershift(_pad_a, i)]
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input_ids_AT.append(torch.tensor(input_ids_item))
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input_id_T = torch.tensor([_input_t] + [_pad_t] * T + [_eot, _answer_t])
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input_ids_AT.append(input_id_T)
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input_ids = [input_ids_AA, input_ids_AT]
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stacked_inputids = [[] for _ in range(8)]
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for i in range(2):
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for j in range(8):
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stacked_inputids[j].append(input_ids[i][j])
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stacked_inputids = [torch.stack(tensors) for tensors in stacked_inputids]
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return torch.stack([audio_feature, audio_feature]), stacked_inputids
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def load_audio(path):
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audio = whisper.load_audio(path)
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duration_ms = (len(audio) / 16000) * 1000
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audio = whisper.pad_or_trim(audio)
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mel = whisper.log_mel_spectrogram(audio)
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return mel, int(duration_ms / 20) + 1
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def A1_A2_batch(fabric, audio_feature, input_ids, leng, model, text_tokenizer, step,
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snacmodel, out_dir=None):
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with fabric.init_tensor():
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model.set_kv_cache(batch_size=2)
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tokenlist = generate_TA_BATCH(
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model,
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audio_feature,
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input_ids,
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[leng, leng],
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["A1A2", "A1T2"],
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max_returned_tokens=2048,
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temperature=0.9,
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top_k=1,
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eos_id_a=_eoa,
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eos_id_t=_eot,
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pad_id_t=_pad_t,
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shift=padded_text_vocabsize,
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include_prompt=True,
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generate_text=True,
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)
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text_tokenlist = tokenlist[-1]
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if text_vocabsize in text_tokenlist:
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text_tokenlist = text_tokenlist[: text_tokenlist.index(text_vocabsize)]
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text = text_tokenizer.decode(torch.tensor(text_tokenlist)).strip()
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audio_tokenlist = tokenlist[:-1]
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audiolist = reconscruct_snac(audio_tokenlist)
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audio = reconstruct_tensors(audiolist)
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if out_dir is None:
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out_dir = "./output/default/A1-A2-batch"
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else:
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out_dir = out_dir + "/A1-A2-batch"
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if not os.path.exists(out_dir):
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os.makedirs(out_dir)
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with torch.inference_mode():
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audio_hat = snacmodel.decode(audio)
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sf.write(
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f"{out_dir}/{step:02d}.wav",
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audio_hat.squeeze().cpu().numpy(),
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24000,
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)
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model.clear_kv_cache()
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return text
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def A1_T2(fabric, audio_feature, input_ids, leng, model, text_tokenizer, step):
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with fabric.init_tensor():
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model.set_kv_cache(batch_size=1)
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tokenlist = generate_AT(
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model,
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audio_feature,
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input_ids,
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[leng],
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["AT"],
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max_returned_tokens=2048,
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temperature=0.9,
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top_k=1,
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eos_id_a=_eoa,
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eos_id_t=_eot,
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pad_id_t=_pad_t,
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shift=padded_text_vocabsize,
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include_prompt=True,
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generate_text=True,
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)
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return text_tokenizer.decode(torch.tensor(tokenlist)).strip()
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def A1_A2(fabric, audio_feature, input_ids, leng, model, text_tokenizer, step,
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snacmodel, out_dir=None):
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with fabric.init_tensor():
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model.set_kv_cache(batch_size=1)
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tokenlist = generate_AA(
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model,
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audio_feature,
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input_ids,
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[leng],
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["A1T2"],
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max_returned_tokens=2048,
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temperature=0.9,
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top_k=1,
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eos_id_a=_eoa,
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eos_id_t=_eot,
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pad_id_t=_pad_t,
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shift=padded_text_vocabsize,
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include_prompt=True,
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generate_text=True,
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)
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audiolist = reconscruct_snac(tokenlist)
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tokenlist = tokenlist[-1]
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if text_vocabsize in tokenlist:
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tokenlist = tokenlist[: tokenlist.index(text_vocabsize)]
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if out_dir is None:
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out_dir = "./output/default/A1-A2"
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else:
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out_dir = out_dir + "/A1-A2"
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if not os.path.exists(out_dir):
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os.makedirs(out_dir)
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audio = reconstruct_tensors(audiolist)
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with torch.inference_mode():
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audio_hat = snacmodel.decode(audio)
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sf.write(
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f"{out_dir}/{step:02d}.wav",
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audio_hat.squeeze().cpu().numpy(),
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24000,
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)
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model.clear_kv_cache()
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return text_tokenizer.decode(torch.tensor(tokenlist)).strip()
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def A1_T1(fabric, audio_feature, input_ids, leng, model, text_tokenizer, step):
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with fabric.init_tensor():
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model.set_kv_cache(batch_size=1)
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tokenlist = generate_ASR(
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model,
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audio_feature,
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input_ids,
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[leng],
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["A1T1"],
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max_returned_tokens=2048,
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temperature=0.9,
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top_k=1,
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eos_id_a=_eoa,
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eos_id_t=_eot,
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pad_id_t=_pad_t,
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shift=padded_text_vocabsize,
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include_prompt=True,
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generate_text=True,
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)
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model.clear_kv_cache()
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return text_tokenizer.decode(torch.tensor(tokenlist)).strip()
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def T1_A2(fabric, input_ids, model, text_tokenizer, step,
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snacmodel, out_dir=None):
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with fabric.init_tensor():
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model.set_kv_cache(batch_size=1)
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tokenlist = generate_TA(
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model,
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None,
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input_ids,
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None,
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["T1A2"],
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max_returned_tokens=2048,
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temperature=0.9,
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top_k=1,
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eos_id_a=_eoa,
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eos_id_t=_eot,
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pad_id_t=_pad_t,
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shift=padded_text_vocabsize,
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include_prompt=True,
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generate_text=True,
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)
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audiolist = reconscruct_snac(tokenlist)
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tokenlist = tokenlist[-1]
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if text_vocabsize in tokenlist:
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tokenlist = tokenlist[: tokenlist.index(text_vocabsize)]
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audio = reconstruct_tensors(audiolist)
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if out_dir is None:
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out_dir = "./output/default/T1-A2"
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else:
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out_dir = out_dir + "/T1-A2"
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if not os.path.exists(out_dir):
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os.makedirs(out_dir)
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with torch.inference_mode():
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audio_hat = snacmodel.decode(audio)
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sf.write(
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f"{out_dir}/{step:02d}.wav",
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audio_hat.squeeze().cpu().numpy(),
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24000,
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)
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model.clear_kv_cache()
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return text_tokenizer.decode(torch.tensor(tokenlist)).strip()
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def T1_T2(fabric, input_ids, model, text_tokenizer, step):
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with fabric.init_tensor():
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model.set_kv_cache(batch_size=1)
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tokenlist = generate_TT(
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model,
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None,
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input_ids,
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None,
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["T1T2"],
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max_returned_tokens=2048,
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temperature=0.9,
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top_k=1,
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eos_id_a=_eoa,
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eos_id_t=_eot,
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pad_id_t=_pad_t,
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shift=padded_text_vocabsize,
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include_prompt=True,
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generate_text=True,
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)
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model.clear_kv_cache()
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return text_tokenizer.decode(torch.tensor(tokenlist)).strip()
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def load_model(ckpt_dir, device):
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snacmodel = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().to(device)
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whisper_model_path = ckpt_dir + "/small.pt"
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if not os.path.exists(whisper_model_path):
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whisper_model_path = "small"
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whispermodel = whisper.load_model(whisper_model_path).to(device)
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text_tokenizer = Tokenizer(ckpt_dir)
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fabric = L.Fabric(devices=1, strategy="auto")
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config = Config.from_file(ckpt_dir + "/model_config.yaml")
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config.post_adapter = False
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with fabric.init_module(empty_init=False):
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model = GPT(config)
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model = fabric.setup(model)
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state_dict = lazy_load(ckpt_dir + "/lit_model.pth")
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model.load_state_dict(state_dict, strict=True)
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model.to(device).eval()
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return fabric, model, text_tokenizer, snacmodel, whispermodel
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def download_model(ckpt_dir):
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repo_id = "gpt-omni/mini-omni2"
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snapshot_download(repo_id, local_dir=ckpt_dir, revision="main")
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def get_text_stream(list_output, index, text_tokenizer):
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text_tokens = list_output[-1][index:]
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index += len(text_tokens)
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is_text_end = False
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if text_vocabsize in text_tokens:
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text_tokens = text_tokens[:text_tokens.index(text_vocabsize)]
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is_text_end = True
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if len(text_tokens) == 0:
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return "", index, is_text_end
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res_text = text_tokenizer.decode(torch.tensor(text_tokens))
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return res_text, index, is_text_end
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|
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|
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class OmniInference:
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def __init__(self, ckpt_dir='./checkpoint', device='cuda:0'):
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self.device = device
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if not os.path.exists(ckpt_dir):
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print(f"checkpoint directory {ckpt_dir} not found, downloading from huggingface")
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download_model(ckpt_dir)
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self.fabric, self.model, self.text_tokenizer, self.snacmodel, self.whispermodel = load_model(ckpt_dir, device)
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def warm_up(self, sample='./data/samples/output1.wav'):
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for _ in self.run_AT_batch_stream(sample):
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pass
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|
|
@torch.inference_mode()
|
|
def run_AT_batch_stream(self,
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audio_path,
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stream_stride=4,
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max_returned_tokens=2048,
|
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temperature=0.9,
|
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top_k=1,
|
|
top_p=1.0,
|
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eos_id_a=_eoa,
|
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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)
|
|
|
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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']
|
|
|
|
|
|
|
|
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?",
|
|
]
|
|
|
|
|
|
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()
|
|
|
