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inference.py
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from __future__ import print_function
import argparse
import os
import json
import queue
import torch
import yaml
import threading
import struct
import time
import torchaudio
import datetime
import builtins
import math
import soundfile as sf
import numpy as np
import torch.nn.functional as F
import torchaudio.compliance.kaldi as k
from torch.utils.data import DataLoader
from models.pipeline import inferencePipeline
from models.decoder.llm2tts import llm2TTS
from web.parms import GlobalParams
from web.pool import TTSObjectPool
def get_args():
parser = argparse.ArgumentParser(description='Freeze-Omni')
parser.add_argument('--model_path', required=True, help='model_path to load')
parser.add_argument('--llm_path', required=True, help='llm_path to load')
parser.add_argument('--top_k', type=int, default=5)
parser.add_argument('--top_p', type=float, default=0.8)
parser.add_argument('--temperature', type=float, default=0.7)
parser.add_argument('--input_wav', required=True, help='input wav')
parser.add_argument('--output_wav', required=True, help='output wav')
args = parser.parse_args()
print(args)
return args
class audioEncoderProcessor:
def __init__(self, chunk_size = 16):
self.chunk_size = 16
self.chunk_overlap = 3
self.feat_dim = 80
self.frame_size = 400
self.frame_shift = 160
self.frame_overlap = self.frame_size - self.frame_shift
self.CHUNK = self.frame_shift * self.chunk_size
self.reset()
def get_chunk_size(self):
return self.CHUNK
def reset(self):
self.input_chunk = torch.zeros([1, self.chunk_size + self.chunk_overlap, self.feat_dim])
self.input_sample = torch.zeros([1, self.CHUNK + self.frame_overlap , 1])
def fbank_shift(self, sample_data):
# fbank feature shift
self.input_sample[:, :self.frame_overlap , :] = self.input_sample[:, -self.frame_overlap:, :].clone()
self.input_sample[:, self.frame_overlap:, :] = sample_data
def chunk_data_shift(self, xs):
# chunk feature shift
self.input_chunk[:, :self.chunk_overlap, :] = self.input_chunk[:, -self.chunk_overlap:, :].clone()
self.input_chunk[:, self.chunk_overlap:, :] = xs.squeeze(0)
def process(self,
audio: torch.Tensor):
with torch.no_grad():
sample_data = torch.tensor(audio).reshape(1, -1, 1)[:, :, :1] * 32768
self.fbank_shift(sample_data)
# use kaldi api to compute fbank
xs = k.fbank(waveform = self.input_sample.squeeze(-1), dither=0,
frame_length=25, frame_shift=10, num_mel_bins=self.feat_dim)
self.chunk_data_shift(xs)
return self.input_chunk.clone()
def decoder(cur_hidden_state, pipeline, cur_text, tts, codec_chunk_size, codec_padding_size, decoder_topk, wav):
hidden_state_output = torch.cat(cur_hidden_state).squeeze(1)
cur_text_procced = pipeline.post_process(cur_text)
print("Synthesis: ", [cur_text_procced])
embeddings = pipeline.model.llm_decoder.model.embed_tokens(
torch.tensor(pipeline.model.tokenizer.encode(cur_text_procced)).cuda()
)
for seg in tts.run(embeddings.reshape(-1, 896).unsqueeze(0), decoder_topk,
hidden_state_output.reshape(-1, 896).unsqueeze(0),
codec_chunk_size, codec_padding_size):
wav.append(seg)
def inference(pipeline, audio_processor, tts, configs):
"""
Perform inference for a speech dialogue system.
Parameters:
- pipeline: Speech dialogue pipeline.
- audio_processor: Processes raw audio data into a format suitable for the pipeline.
- tts: The speech decoder moudule.
- configs: Input args.
Returns:
- None
"""
wav, fs = sf.read(configs.input_wav)
wav = torch.tensor(wav)
if fs != 16000:
wav = torchaudio.transforms.Resample(orig_freq=fs, new_freq=16000)(wav.float())
fs = 16000
codec_chunk_size = 40
codec_padding_size = 10
decoder_topk = 2
# Satge0: preprocess
# set system role, stat will be set to 'sl'
stat = 'pre'
outputs = pipeline.speech_dialogue(None, stat=stat, role="You are a helpful assistant.")
chunk_size = audio_processor.get_chunk_size()
# Satge1: start listen
# stat will be auto set to 'cl' after Stage1
wav_input = torch.zeros(math.ceil(wav.shape[0] / chunk_size) * chunk_size)
wav_input[:wav.shape[0]] = wav
for i in range(0, wav_input.shape[0], chunk_size):
fbank = audio_processor.process(wav_input[i:i+chunk_size])
outputs = pipeline.speech_dialogue(fbank, **outputs)
outputs['stat'] = 'cl'
audio_processor.reset()
outputs['adapter_cache'] = None
outputs['encoder_cache'] = None
outputs['pe_index'] = 0
outputs['stat'] = 'ss'
# Stage3: start speak
outputs = pipeline.speech_dialogue(None, **outputs)
cur_hidden_state = []
cur_hidden_state.append(outputs['hidden_state'])
whole_text = ''
last_text = ''
cur_text = ''
wav = []
# Stage4: contiune speak until stat is set to 'sl'
# use 'stop' to interrupt generation, stat need to be manually set as 'sl'
stop = False
while True:
if len(outputs['past_tokens']) > 128:
stop = True
if stop:
break
del outputs['text']
del outputs['hidden_state']
outputs = pipeline.speech_dialogue(None, **outputs)
if outputs['stat'] == 'cs':
cur_hidden_state.append(outputs['hidden_state'])
whole_text += outputs['text'][len(last_text):]
cur_text += outputs['text'][len(last_text):]
suffix_list = ["。", "：", "？", "！", ".", "?","!", "\n"]
if outputs['text'][len(last_text):].endswith(tuple(suffix_list)):
if outputs['text'][len(last_text):].endswith(".") and last_text[-1].isdigit():
pass
else:
if len(cur_hidden_state) > 0:
decoder(cur_hidden_state, pipeline, cur_text, tts,
codec_chunk_size, codec_padding_size, decoder_topk, wav)
cur_hidden_state = []
cur_text = ""
if outputs['stat'] == 'sl':
break
# print(outputs['text'])
last_text = outputs['text']
if len(cur_hidden_state) != 0:
decoder(cur_hidden_state, pipeline, cur_text, tts,
codec_chunk_size, codec_padding_size, decoder_topk, wav)
sf.write(configs.output_wav, torch.cat(wav, -1).squeeze().float().cpu().numpy(), 24000)
outputs['stat'] = 'sl'
outputs['last_id'] = None
print(whole_text)
if __name__ == '__main__':
configs = get_args()
pipeline = inferencePipeline(configs)
tts = llm2TTS(configs.model_path)
audio_processor = audioEncoderProcessor()
inference(pipeline, audio_processor, tts, configs)