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T4
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import gradio as gr
from huggingface_hub import snapshot_download
from threading import Thread
import os
import time
import gradio as gr
import base64
import numpy as np
import requests
import traceback
from server import serve
repo_id = "gpt-omni/mini-omni"
snapshot_download(repo_id, local_dir="./checkpoint", revision="main")
IP='0.0.0.0'
PORT=60808
thread = Thread(target=serve, daemon=True)
thread.start()
API_URL = "http://0.0.0.0:60808/chat"
# recording parameters
IN_CHANNELS = 1
IN_RATE = 24000
IN_CHUNK = 1024
IN_SAMPLE_WIDTH = 2
VAD_STRIDE = 0.5
# playing parameters
OUT_CHANNELS = 1
OUT_RATE = 24000
OUT_SAMPLE_WIDTH = 2
OUT_CHUNK = 5760
OUT_CHUNK = 4096
OUT_RATE = 24000
OUT_CHANNELS = 1
def run_vad(ori_audio, sr):
_st = time.time()
try:
audio = np.frombuffer(ori_audio, dtype=np.int16)
audio = audio.astype(np.float32) / 32768.0
sampling_rate = 16000
if sr != sampling_rate:
audio = librosa.resample(audio, orig_sr=sr, target_sr=sampling_rate)
vad_parameters = {}
vad_parameters = VadOptions(**vad_parameters)
speech_chunks = get_speech_timestamps(audio, vad_parameters)
audio = collect_chunks(audio, speech_chunks)
duration_after_vad = audio.shape[0] / sampling_rate
if sr != sampling_rate:
# resample to original sampling rate
vad_audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=sr)
else:
vad_audio = audio
vad_audio = np.round(vad_audio * 32768.0).astype(np.int16)
vad_audio_bytes = vad_audio.tobytes()
return duration_after_vad, vad_audio_bytes, round(time.time() - _st, 4)
except Exception as e:
msg = f"[asr vad error] audio_len: {len(ori_audio)/(sr*2):.3f} s, trace: {traceback.format_exc()}"
print(msg)
return -1, ori_audio, round(time.time() - _st, 4)
def warm_up():
frames = b"\x00\x00" * 1024 * 2 # 1024 frames of 2 bytes each
dur, frames, tcost = run_vad(frames, 16000)
print(f"warm up done, time_cost: {tcost:.3f} s")
warm_up()
def determine_pause(stream: bytes, start_talking: bool) -> tuple[bytes, bool]:
"""Take in the stream, determine if a pause happened"""
temp_audio = stream
if len(temp_audio) > IN_SAMPLE_WIDTH * IN_RATE * IN_CHANNELS * VAD_STRIDE:
dur_vad, vad_audio_bytes, time_vad = run_vad(temp_audio, IN_RATE)
print(f"duration_after_vad: {dur_vad:.3f} s, time_vad: {time_vad:.3f} s")
if dur_vad > 0.2 and not start_talking:
if last_temp_audio is not None:
st.session_state.frames.append(last_temp_audio)
start_talking = True
if start_talking:
st.session_state.frames.append(temp_audio)
if dur_vad < 0.1 and start_talking:
st.session_state.recording = False
print(f"speech end detected. excit")
last_temp_audio = temp_audio
temp_audio = b""
def process_audio(audio):
filepath = audio
print(f"filepath: {filepath}")
if filepath is None:
return
cnt = 0
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}")
def greet(name):
return "Hello " + name + "!!"
demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch()
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