Mac M1下运行端到端语音模型Mini-Omni
Mini-Omni的贡献是让任意大模型都可以具有“听”和“说”的能力。
✅实时语音到语音对话能力。不需要额外的ASR或TTS模型。
✅边说边思考，能够同时生成文本和音频。
✅流媒体音频输出功能。
✅通过“音频到文本”和“音频到音频”批量推理来进一步提高性能。
工作原理：
一、准备虚拟环境omni，下载代码，安装依赖：
conda create -n omni python=3.10
conda activate omni
git clone https://github.com/gpt-omni/mini-omni.git
cd mini-omni
pip install -r requirements.txt二、设置下载镜像，下载模型到./checkpoint目录：
export HF_ENDPOINT=https://hf-mirror.com
huggingface-cli download --resume-download gpt-omni/mini-omni --local-dir ./checkpoint三、默认使用的是GPU(cuda:0)，Mac M1没有GPU，Pytorch下Mac M1默认的是mps，但Whisper模型不支持mps运行，咱们使用cpu运行。需要将server.py，inference.py，webui/omni-gradio.py中'cuda:0'的值修改为'cpu'。
四、安装ffmpeg
# 安装 ffmpeg
brew install ffmpeg
# 查看版本
ffmpeg -version五、运行服务端：
python3 server.py --ip '0.0.0.0' --port 60808六、运行gradio客户端：
API_URL=http://0.0.0.0:60808/chat python3 webui/omni_gradio.py七、修改的代码
1、server.py
import sys
import os
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from inference import OmniInference
import flask
import base64
import tempfile
import traceback
from flask import Flask, Response, stream_with_context
class OmniChatServer(object):
def __init__(self, ip='0.0.0.0', port=60808, run_app=True,
ckpt_dir='./checkpoint', device='cpu') -> None:
server = Flask(__name__)
# CORS(server, resources=r"/*")
# server.config["JSON_AS_ASCII"] = False
self.client = OmniInference(ckpt_dir, device)
self.client.warm_up()
server.route("/chat", methods=["POST"])(self.chat)
if run_app:
server.run(host=ip, port=port, threaded=False)
else:
self.server = server
def chat(self) -> Response:
req_data = flask.request.get_json()
try:
data_buf = req_data["audio"].encode("utf-8")
data_buf = base64.b64decode(data_buf)
stream_stride = req_data.get("stream_stride", 4)
max_tokens = req_data.get("max_tokens", 2048)
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
f.write(data_buf)
audio_generator = self.client.run_AT_batch_stream(f.name, stream_stride, max_tokens)
return Response(stream_with_context(audio_generator), mimetype="audio/wav")
except Exception as e:
print(traceback.format_exc())
# CUDA_VISIBLE_DEVICES=1 gunicorn -w 2 -b 0.0.0.0:60808 'server:create_app()'
def create_app():
server = OmniChatServer(run_app=False)
return server.server
def serve(ip='0.0.0.0', port=60808, device='cpu'):
OmniChatServer(ip, port=port,run_app=True, device=device)
if __name__ == "__main__":
import fire
fire.Fire(serve)
2、inference.py
import os
import lightning as L
import torch
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_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
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)
whispermodel = whisper.load_model("small").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-omni"
snapshot_download(repo_id, local_dir=ckpt_dir, revision="main")
class OmniInference:
def __init__(self, ckpt_dir='./checkpoint', device='cpu'):
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,
):
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_batch(
model,
audio_feature.to(torch.float32).to(model.device),
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
for _ in tqdm(range(2, max_returned_tokens - T + 1)):
tokens_A, token_T = next_token_batch(
model,
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)
yield audio_stream
input_pos = input_pos.add_(1)
index += 1
text = self.text_tokenizer.decode(torch.tensor(list_output[-1]))
print(f"text output: {text}")
model.clear_kv_cache()
return list_output
def test_infer():
device = "cpu"
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(os.listdir('./data/samples'))
test_audio_list = [os.path.join('./data/samples', path) for path in test_audio_list]
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()
3、webui/omni_gradio.py
"""A simple web interactive chat demo based on gradio."""
import os
import time
import gradio as gr
import base64
import numpy as np
import requests
API_URL = os.getenv("API_URL", None)
client = None
if API_URL is None:
from inference import OmniInference
omni_client = OmniInference('./checkpoint', 'cpu')
omni_client.warm_up()
OUT_CHUNK = 4096
OUT_RATE = 24000
OUT_CHANNELS = 1
def process_audio(audio):
filepath = audio
print(f"filepath: {filepath}")
if filepath is None:
return
cnt = 0
if API_URL is not None:
with open(filepath, "rb") as f:
data = f.read()
base64_encoded = str(base64.b64encode(data), encoding="utf-8")
files = {"audio": base64_encoded}
tik = time.time()
with requests.post(API_URL, json=files, stream=True) as response:
try:
for chunk in response.iter_content(chunk_size=OUT_CHUNK):
if chunk:
# Convert chunk to numpy array
if cnt == 0:
print(f"first chunk time cost: {time.time() - tik:.3f}")
cnt += 1
audio_data = np.frombuffer(chunk, dtype=np.int16)
audio_data = audio_data.reshape(-1, OUT_CHANNELS)
yield OUT_RATE, audio_data.astype(np.int16)
except Exception as e:
print(f"error: {e}")
else:
tik = time.time()
for chunk in omni_client.run_AT_batch_stream(filepath):
# Convert chunk to numpy array
if cnt == 0:
print(f"first chunk time cost: {time.time() - tik:.3f}")
cnt += 1
audio_data = np.frombuffer(chunk, dtype=np.int16)
audio_data = audio_data.reshape(-1, OUT_CHANNELS)
yield OUT_RATE, audio_data.astype(np.int16)
def main(port=None):
demo = gr.Interface(
process_audio,
inputs=gr.Audio(type="filepath", label="Microphone"),
outputs=[gr.Audio(label="Response", streaming=True, autoplay=True)],
title="Chat Mini-Omni Demo",
live=True,
)
if port is not None:
demo.queue().launch(share=False, server_name="0.0.0.0", server_port=port)
else:
demo.queue().launch()
if __name__ == "__main__":
import fire
fire.Fire(main(port=7860))