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upgraded lollms core

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This commit is contained in:
Saifeddine ALOUI 2024-01-15 01:10:29 +01:00
parent d970f80325
commit 6c2199b5f3
4 changed files with 93 additions and 160 deletions
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9
configs/config.yaml
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@ -1,5 +1,5 @@
# =================== Lord Of Large Language Models Configuration file ===========================
version: 45
version: 46
binding_name: null
model_name: null
@ -42,6 +42,13 @@ db_path: database.db
# Automatic updates
debug: False
auto_update: true
auto_sync_personalities: true
auto_sync_extensions: true
auto_sync_bindings: true
auto_sync_models: true
auto_save: true
auto_title: false
# Install mode (cpu, cpu-noavx, nvidia-tensorcores, nvidia, amd-noavx, amd, apple-intel, apple-silicon)

9
lollms/configs/config.yaml
View File

@ -1,5 +1,5 @@
# =================== Lord Of Large Language Models Configuration file ===========================
version: 45
version: 46
binding_name: null
model_name: null
@ -42,6 +42,13 @@ db_path: database.db
# Automatic updates
debug: False
auto_update: true
auto_sync_personalities: true
auto_sync_extensions: true
auto_sync_bindings: true
auto_sync_models: true
auto_save: true
auto_title: false
# Install mode (cpu, cpu-noavx, nvidia-tensorcores, nvidia, amd-noavx, amd, apple-intel, apple-silicon)

201
lollms/media.py
View File

@ -8,8 +8,10 @@ License: Apache 2.0
"""
from lollms.utilities import PackageManager
from lollms.com import LoLLMsCom
from lollms.utilities import trace_exception, run_async
from ascii_colors import ASCIIColors
import platform
from functools import partial
import subprocess
import os
@ -46,12 +48,8 @@ if not PackageManager.check_package_installed("whisper"):
PackageManager.install_package("openai-whisper")
import whisper
import socketio
from lollms.com import LoLLMsCom
import time
import json
import base64
import io
import numpy as np
try:
if not PackageManager.check_package_installed("sounddevice"):
# os.system("sudo apt-get install portaudio19-dev")
@ -66,177 +64,96 @@ try:
import wave
except:
ASCIIColors.error("Couldn't load sound tools")
import time
import base64
import io
import socketio
from scipy.io.wavfile import write
from matplotlib import pyplot as plt
import numpy as np
from scipy.signal import spectrogram
class AudioRecorder:
def __init__(self, socketio, filename, channels=1, sample_rate=16000, chunk_size=24678, silence_threshold=150.0, silence_duration=2, callback=None, lollmsCom=None):
try:
self.socketio = socketio
def __init__(self, sio:socketio.Client, filename, channels=1, sample_rate=16000, chunk_size=24678, silence_threshold=150.0, silence_duration=2, callback=None, lollmsCom:LoLLMsCom=None):
self.sio = sio
self.sio = sio
self.filename = filename
self.channels = channels
self.sample_rate = sample_rate
self.chunk_size = chunk_size
self.audio_stream = None
self.audio_frames = []
self.is_recording = False
self.silence_threshold = silence_threshold
self.silence_duration = silence_duration
self.last_sound_time = time.time()
self.callback = callback
self.lollmsCom = lollmsCom
self.whisper_model = None
except:
self.socketio = socketio
self.filename = filename
self.channels = channels
self.sample_rate = sample_rate
self.chunk_size = chunk_size
self.audio_stream = None
self.audio_frames = []
self.buffer = []
self.is_recording = False
self.silence_threshold = silence_threshold
self.silence_duration = silence_duration
self.start_time = time.time()
self.last_sound_time = time.time()
self.callback = callback
self.lollmsCom = lollmsCom
self.whisper_model = None
def audio_callback(self, indata, frames, time_, status):
volume_norm = np.linalg.norm(indata)*10
# if volume_norm > self.silence_threshold:
# self.last_sound_time = time.time()
# if not self.is_recording:
# self.is_recording = True
# self.start_time = time.time()
if self.is_recording:
self.buffer = np.append(self.buffer, indata.copy())
# if time.time() - self.last_sound_time > self.silence_duration:
# self.stop_recording()
def start_recording(self):
if self.whisper_model is None:
self.lollmsCom.info("Loading whisper model")
self.whisper_model=whisper.load_model("base.en")
try:
self.is_recording = True
self.audio_stream = sd.InputStream(
channels=self.channels,
samplerate=self.sample_rate,
callback=self._record,
blocksize=self.chunk_size
)
self.buffer = np.array([], dtype=np.float32)
self.audio_stream = sd.InputStream(callback=self.audio_callback, channels=self.channels, samplerate=self.sample_rate)
self.audio_stream.start()
self.lollmsCom.info("Recording started...")
except:
self.lollmsCom.error("No audio input found!")
def _record(self, indata, frames, time_, status):
first_recording = True # Flag to track the first recording
silence_duration = 5
non_silent_start = None
non_silent_end = None
last_spectrogram_update = time.time()
self.audio_frames = None
buffered = np.array(indata)
if self.audio_frames is not None:
self.audio_frames = np.concatenate([self.audio_frames, buffered])
else:
self.audio_frames = buffered
# Remove audio frames that are older than 30 seconds
if len(self.audio_frames) > self.sample_rate * 30:
self.audio_frames=self.audio_frames[-self.sample_rate * 30:]
# Update spectrogram every 3 seconds
if time.time() - last_spectrogram_update >= 1:
self._update_spectrogram()
last_spectrogram_update = time.time()
# Check for silence
rms = self._calculate_rms(buffered)
if rms < self.silence_threshold:
current_time = time.time()
if current_time - self.last_sound_time >= silence_duration:
if first_recording:
first_recording = False
silence_duration = self.silence_duration
if self.callback and non_silent_start is not None and non_silent_end - non_silent_start >= 1:
self.lollmsCom.info("Analyzing")
# Convert to float
audio_data = self.audio_frames.astype(np.float32)
audio = wave.open(str(self.filename), 'wb')
audio.setnchannels(self.channels)
audio.setsampwidth(self.audio_stream.dtype.itemsize)
audio.setframerate(self.sample_rate)
audio.writeframes(b''.join(self.audio_frames[non_silent_start:non_silent_end]))
audio.close()
# Transcribe the audio using the whisper model
text = self.whisper_model.transcribe(audio_data[non_silent_start:non_silent_end])
self.callback(text)
print(text["text"])
self.last_sound_time = time.time()
non_silent_start = None
else:
self.last_sound_time = time.time()
if non_silent_start is None:
non_silent_start = len(self.audio_frames) - 1
non_silent_end = len(self.audio_frames)
def _update_spectrogram(self):
audio_data = self.audio_frames[-self.sample_rate*30:]
frequencies, _, spectrogram = signal.spectrogram(audio_data, self.sample_rate)
# Generate a new times array that only spans the last 30 seconds
times = np.linspace(0, 30, spectrogram.shape[1])
# Plot spectrogram
plt.figure(figsize=(10, 4))
plt.imshow(np.log(spectrogram), aspect='auto', origin='lower', cmap='inferno', extent=[times.min(), times.max(), frequencies.min(), frequencies.max()])
plt.xlabel('Time')
plt.ylabel('Frequency')
plt.title('Spectrogram')
plt.colorbar(format='%+2.0f dB')
# Convert plot to base64 image
img_buffer = io.BytesIO()
plt.savefig(img_buffer, format='png')
img_buffer.seek(0)
img_base64 = base64.b64encode(img_buffer.getvalue()).decode('utf-8')
# Send base64 image using socketio
self.socketio.emit('update_spectrogram', img_base64)
self.socketio.sleep(0.0)
plt.close()
def _calculate_rms(self, data):
try:
squared_sum = sum([sample ** 2 for sample in data])
rms = np.sqrt(squared_sum / len(data))
except:
rms = 0
return rms
except Exception as ex:
self.lollmsCom.InfoMessage("Couldn't start recording.\nMake sure your input device is connected and operational")
trace_exception(ex)
def stop_recording(self):
self.is_recording = False
if self.audio_stream:
self.audio_stream.stop()
import wave
audio = wave.open(str(self.filename), 'wb')
audio.setnchannels(self.channels)
audio.setsampwidth(self.audio_stream.dtype.itemsize)
audio.setframerate(self.sample_rate)
audio.writeframes(b''.join(self.audio_frames))
audio.close()
self.audio_stream.close()
write(self.filename, self.sample_rate, self.buffer)
self.lollmsCom.info(f"Saved to {self.filename}")
time.sleep(2)
self.lollmsCom.info(f"Transcribing ... ")
result = self.whisper_model.transcribe(str(self.filename))
with open(self.filename.replace("wav","txt"), "w") as f:
f.write(result["text"])
self.lollmsCom.info(f"Saved to {self.filename}")
self.lollmsCom.info(f"Recording saved to {self.filename}")
else:
self.warning("No recording available")
def update_spectrogram(self):
f, t, Sxx = spectrogram(self.buffer[-30*self.sample_rate:], self.sample_rate)
plt.pcolormesh(t, f, 10 * np.log10(Sxx))
buf = io.BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
image_base64 = base64.b64encode(buf.read())
self.sio.emit('update_spectrogram', {'image': image_base64.decode('utf-8')})
self.last_spectrogram_update = time.perf_counter()
buf.close()
plt.clf()
class WebcamImageSender:
"""
Class for capturing images from the webcam and sending them to a SocketIO client.
"""
def __init__(self, socketio, lollmsCom:LoLLMsCom=None):
def __init__(self, sio:socketio, lollmsCom:LoLLMsCom=None):
"""
Initializes the WebcamImageSender class.
Args:
socketio (socketio.Client): The SocketIO client object.
"""
self.socketio = socketio
self.socketio = sio
self.last_image = None
self.last_change_time = None
self.capture_thread = None
@ -278,8 +195,9 @@ class WebcamImageSender:
self.socketio.emit("video_stream_image", image_base64.decode('utf-8'))
cap.release()
except:
except Exception as ex:
self.lollmsCom.error("Couldn't start webcam")
trace_exception(ex)
def image_difference(self, image):
"""
@ -349,5 +267,6 @@ class MusicPlayer(threading.Thread):
"""
Stops the music.
"""
import pygame
self.stopped = True
pygame.mixer.music.stop()

10
lollms/server/endpoints/lollms_personalities_infos.py
View File

@ -17,7 +17,7 @@ from lollms.personality import AIPersonality, InstallOption
from ascii_colors import ASCIIColors
from lollms.utilities import load_config, trace_exception, gc
from pathlib import Path
from typing import List
from typing import List, Optional
import psutil
import yaml
@ -26,10 +26,6 @@ import yaml
class PersonalityListingInfos(BaseModel):
category:str
class PersonalityMountingInfos(BaseModel):
category:str
folder:str
language:str
class PersonalitySelectionInfos(BaseModel):
id:int
@ -237,6 +233,10 @@ def clear_personality_files_list():
# ------------------------------------------- Mounting/Unmounting/Remounting ------------------------------------------------
class PersonalityMountingInfos(BaseModel):
category:str
folder:str
language:Optional[str] = None
@router.post("/mount_personality")
def mount_personality(data:PersonalityMountingInfos):