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This commit is contained in:
maglore9900
parent
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commit
d195d63580
@ -1,6 +1,6 @@
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from typing import TypedDict, Annotated, List, Union
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import operator
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from modules import adapter, spotify, app_launcher, windows_focus, sp_test2
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from modules import adapter, speak, spotify, app_launcher, windows_focus
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from langchain_core.agents import AgentAction, AgentFinish
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from langchain.agents import create_openai_tools_agent
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from langchain.prompts import PromptTemplate, SystemMessagePromptTemplate
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@ -19,8 +19,7 @@ class Agent:
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self.ap = app_launcher.AppLauncher()
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self.wf = windows_focus.WindowFocusManager()
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self.llm = self.ad.llm_chat
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# self.spk = speak.Speak()
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self.spk = sp_test2.Speak(model="whisper")
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self.spk = speak.Speak(model="whisper")
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# Pull the template
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self.prompt = hub.pull("hwchase17/openai-functions-agent")
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self.max_prompt = '''
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@ -7,134 +7,123 @@ import speech_recognition as sr
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import pyttsx3
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import os
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import random
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from pydub import AudioSegment
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import urllib.parse
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import requests
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from pydub import AudioSegment
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import json
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# from numpy import frombuffer, int16
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os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
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class Speak:
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def __init__(self, model="whisper"):
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self.url = "http://127.0.0.1:7851/api/tts-generate"
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self.microphone = sr.Microphone()
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self.engine = pyttsx3.init()
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self.engine.setProperty('rate', 150)
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self.model_name = model
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self.sample_rate = 16000
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self.chunk_size = 1024
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self.noise_threshold = 500
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# Initialize transcription models
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self.noise_threshold = 500 # Threshold to detect ambient noise
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# Initialize Vosk and Whisper models
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if self.model_name == "vosk":
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self.model_path = os.path.join(os.path.dirname(__file__), "../models/vosk-model-en-us-0.42-gigaspeech")
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self.model = Model(self.model_path)
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self.recognizer = KaldiRecognizer(self.model, 16000)
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elif self.model_name == "whisper":
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self.whisper_model_path = "large-v2"
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self.whisper_model = WhisperModel(self.whisper_model_path, device="cuda") # Adjust if no CUDA
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self.recognizer = WhisperModel(self.whisper_model_path, device="cuda") # Adjust if you don't have a CUDA-compatible GPU
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# self.recognizer = None
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else:
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self.recognizer = sr.Recognizer()
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def listen_to_microphone(self, time_listen=10):
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"""Function to listen to the microphone input and return raw audio data."""
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def listen3(self, time_listen=10):
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"""
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Streams audio from the microphone and applies noise cancellation.
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"""
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counter = 0
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p = pyaudio.PyAudio()
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stream = p.open(format=pyaudio.paInt16, channels=1, rate=self.sample_rate, input=True, frames_per_buffer=self.chunk_size)
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stream.start_stream()
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print("Listening...")
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audio_data = b""
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ambient_noise_data = b""
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try:
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for i in range(int(self.sample_rate / self.chunk_size * time_listen)):
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audio_chunk = stream.read(self.chunk_size)
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audio_data += audio_chunk
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# Capture ambient noise in the first 2 seconds
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if i < int(self.sample_rate / self.chunk_size * 2): # First 2 seconds
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ambient_noise_data += audio_chunk
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while counter < time_listen:
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# Read audio data from the stream
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audio_data = stream.read(8000, exception_on_overflow=False)
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# Convert the audio data to a numpy array of int16
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audio_np = np.frombuffer(audio_data, dtype=np.int16)
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# Apply noise reduction
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reduced_noise = nr.reduce_noise(y=audio_np, sr=self.sample_rate)
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# Calculate RMS to detect ambient noise levels
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rms_value = np.sqrt(np.mean(np.square(reduced_noise)))
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if rms_value < self.noise_threshold:
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# Pass the reduced noise (still in numpy format) to the transcoder
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self.transcoder(reduced_noise.tobytes())
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else:
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print(f"Ambient noise detected: RMS {rms_value} exceeds threshold {self.noise_threshold}")
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counter += 1
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except KeyboardInterrupt:
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print("Stopping...")
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finally:
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# Clean up the stream resources
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stream.stop_stream()
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stream.close()
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p.terminate()
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return audio_data, ambient_noise_data
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def apply_noise_cancellation(self, audio_data, ambient_noise):
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"""Apply noise cancellation to the given audio data, using ambient noise from the first 2 seconds."""
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# Convert to NumPy array (normalize to [-1, 1])
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audio_np = np.frombuffer(audio_data, np.int16).astype(np.float32) / 32768.0
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ambient_noise_np = np.frombuffer(ambient_noise, np.int16).astype(np.float32) / 32768.0
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# Use ambient noise as noise profile
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reduced_noise = nr.reduce_noise(y=audio_np, sr=self.sample_rate, y_noise=ambient_noise_np)
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# Convert back to int16 after noise reduction for compatibility with Whisper
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reduced_noise_int16 = (reduced_noise * 32768).astype(np.int16)
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return reduced_noise_int16.tobytes() # Return as bytes
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def transcribe(self, audio_data):
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"""Transcribe the audio data using the selected model."""
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if self.model_name == "whisper":
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# # Whisper expects float32 data
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# # Convert int16 PCM back to float32
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# audio_np = np.frombuffer(audio_data, np.int16).astype(np.float32) / 32768.0
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# # Transcribe using Whisper model
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# segments, _ = self.whisper_model.transcribe(audio_np, beam_size=5)
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# transcription = " ".join([segment.text for segment in segments])
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# print(f"Whisper Transcription: {transcription}")
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# return transcription
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# Whisper expects float32 data
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energy_threshold=0.001
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audio_np = np.frombuffer(audio_data, np.int16).astype(np.float32) / 32768.0
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# Calculate energy of the audio to determine if it should be transcribed
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energy = np.mean(np.abs(audio_np))
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# Only transcribe if energy exceeds the threshold
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if energy > energy_threshold:
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# print(f"Audio energy ({energy}) exceeds threshold ({energy_threshold}), proceeding with transcription.")
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segments, _ = self.whisper_model.transcribe(audio_np, beam_size=5)
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transcription = " ".join([segment.text for segment in segments])
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print(f"Whisper Transcription: {transcription}")
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return transcription
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else:
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# print(f"Audio energy ({energy}) is below the threshold ({energy_threshold}), skipping transcription.")
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return ""
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elif self.model_name == "vosk":
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# Convert audio data to bytes for Vosk
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def transcoder(self, audio_data):
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"""
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Transcodes audio data to text using the specified model.
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"""
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if self.model_name == "vosk":
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if self.recognizer.AcceptWaveform(audio_data):
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result = self.recognizer.Result()
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print(f"Vosk Transcription: {result}")
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return result
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result = json.loads(self.recognizer.Result())
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if result["text"]:
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print(f"Recognized: {result['text']}")
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return result['text']
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return result
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elif self.model_name == "whisper":
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result, _ = self.recognizer.transcribe(audio_data, beam_size=5)
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return result['text']
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else:
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# Fallback to default recognizer (for example, speech_recognition module)
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recognizer = sr.Recognizer()
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with sr.AudioFile(audio_data) as source:
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audio = recognizer.record(source)
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try:
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transcription = recognizer.recognize_google(audio)
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print(f"Google Transcription: {transcription}")
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return transcription
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except sr.UnknownValueError:
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print("Google could not understand audio")
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except sr.RequestError as e:
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print(f"Could not request results; {e}")
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result = self.recognizer.recognize_google(audio_data)
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return result
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def listen(self, time_listen=8):
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"""Main transcoder function that handles listening, noise cancellation, and transcription."""
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# Listen to the microphone and get both raw audio and ambient noise
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raw_audio, ambient_noise = self.listen_to_microphone(time_listen)
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# Apply noise cancellation using the ambient noise from the first 2 seconds
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clean_audio = self.apply_noise_cancellation(raw_audio, ambient_noise=ambient_noise)
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# def vosk_transcription(self):
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# """
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# Handles Vosk-based transcription of streamed audio with noise cancellation.
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# """
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# recognizer = KaldiRecognizer(self.vosk_model, self.sample_rate)
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# stream = self.stream_with_noise_cancellation()
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# Transcribe the clean audio
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transcription = self.transcribe(clean_audio)
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# for audio_chunk in stream:
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# if recognizer.AcceptWaveform(audio_chunk):
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# result = recognizer.Result()
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# print(result) # Handle or process the transcription result
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return transcription
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# def whisper_transcription(self):
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# """
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# Handles Faster-Whisper-based transcription of streamed audio with noise cancellation.
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# """
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# stream = self.stream_with_noise_cancellation()
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# for audio_chunk in stream:
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# # Transcribe the cleaned audio using faster-whisper
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# result, _ = self.whisper_model.transcribe(audio_chunk, beam_size=5)
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# print(result['text']) # Handle or process the transcription result
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# def listen(self):
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# if self.model == "vosk":
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# self.vosk_transcription()
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# elif self.model == "whisper":
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# self.whisper_transcription()
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# else:
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# raise ValueError("Invalid model specified. Please specify either 'vosk' or 'whisper'.")
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def glitch_stream_output(self, text):
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def change_pitch(sound, octaves):
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@ -220,8 +209,10 @@ class Speak:
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except:
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self.engine.say(text)
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self.engine.runAndWait()
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# Example usage:
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# sp = Speak(model="vosk") # or "vosk" or "google"
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# transcription = sp.transcoder(time_listen=10)
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# print("Final Transcription:", transcription)
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# sp = Speak(vosk_model_path="path_to_vosk_model", whisper_model_path="large-v2")
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# sp.vosk_transcription() # To start Vosk transcription
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# sp.whisper_transcription() # To start Faster-Whisper transcription
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sp = Speak()
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# sp.glitch_stream_output("Hello, world!")
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sp.listen3()
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173
modules/speak.py
173
modules/speak.py
@ -7,123 +7,134 @@ import speech_recognition as sr
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import pyttsx3
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import os
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import random
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from pydub import AudioSegment
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import urllib.parse
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import requests
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import json
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# from numpy import frombuffer, int16
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from pydub import AudioSegment
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os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
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class Speak:
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def __init__(self, model="whisper"):
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self.url = "http://127.0.0.1:7851/api/tts-generate"
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self.microphone = sr.Microphone()
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self.engine = pyttsx3.init()
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self.engine.setProperty('rate', 150)
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self.model_name = model
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self.sample_rate = 16000
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self.chunk_size = 1024
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self.noise_threshold = 500
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self.noise_threshold = 500 # Threshold to detect ambient noise
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# Initialize Vosk and Whisper models
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# Initialize transcription models
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if self.model_name == "vosk":
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self.model_path = os.path.join(os.path.dirname(__file__), "../models/vosk-model-en-us-0.42-gigaspeech")
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self.model = Model(self.model_path)
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self.recognizer = KaldiRecognizer(self.model, 16000)
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elif self.model_name == "whisper":
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self.whisper_model_path = "large-v2"
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self.recognizer = WhisperModel(self.whisper_model_path, device="cuda") # Adjust if you don't have a CUDA-compatible GPU
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# self.recognizer = None
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self.whisper_model = WhisperModel(self.whisper_model_path, device="cuda") # Adjust if no CUDA
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else:
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self.recognizer = sr.Recognizer()
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def listen3(self, time_listen=10):
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"""
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Streams audio from the microphone and applies noise cancellation.
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"""
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counter = 0
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def listen_to_microphone(self, time_listen=10):
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"""Function to listen to the microphone input and return raw audio data."""
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p = pyaudio.PyAudio()
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stream = p.open(format=pyaudio.paInt16, channels=1, rate=self.sample_rate, input=True, frames_per_buffer=self.chunk_size)
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stream.start_stream()
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print("Listening...")
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audio_data = b""
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ambient_noise_data = b""
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try:
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while counter < time_listen:
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# Read audio data from the stream
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audio_data = stream.read(8000, exception_on_overflow=False)
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# Convert the audio data to a numpy array of int16
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audio_np = np.frombuffer(audio_data, dtype=np.int16)
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# Apply noise reduction
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reduced_noise = nr.reduce_noise(y=audio_np, sr=self.sample_rate)
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# Calculate RMS to detect ambient noise levels
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rms_value = np.sqrt(np.mean(np.square(reduced_noise)))
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if rms_value < self.noise_threshold:
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# Pass the reduced noise (still in numpy format) to the transcoder
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self.transcoder(reduced_noise.tobytes())
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else:
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print(f"Ambient noise detected: RMS {rms_value} exceeds threshold {self.noise_threshold}")
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counter += 1
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except KeyboardInterrupt:
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print("Stopping...")
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for i in range(int(self.sample_rate / self.chunk_size * time_listen)):
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audio_chunk = stream.read(self.chunk_size)
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audio_data += audio_chunk
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# Capture ambient noise in the first 2 seconds
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if i < int(self.sample_rate / self.chunk_size * 2): # First 2 seconds
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ambient_noise_data += audio_chunk
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finally:
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# Clean up the stream resources
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stream.stop_stream()
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stream.close()
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p.terminate()
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def transcoder(self, audio_data):
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"""
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Transcodes audio data to text using the specified model.
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"""
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if self.model_name == "vosk":
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return audio_data, ambient_noise_data
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def apply_noise_cancellation(self, audio_data, ambient_noise):
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"""Apply noise cancellation to the given audio data, using ambient noise from the first 2 seconds."""
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# Convert to NumPy array (normalize to [-1, 1])
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audio_np = np.frombuffer(audio_data, np.int16).astype(np.float32) / 32768.0
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ambient_noise_np = np.frombuffer(ambient_noise, np.int16).astype(np.float32) / 32768.0
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# Use ambient noise as noise profile
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reduced_noise = nr.reduce_noise(y=audio_np, sr=self.sample_rate, y_noise=ambient_noise_np)
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# Convert back to int16 after noise reduction for compatibility with Whisper
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reduced_noise_int16 = (reduced_noise * 32768).astype(np.int16)
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return reduced_noise_int16.tobytes() # Return as bytes
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def transcribe(self, audio_data):
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"""Transcribe the audio data using the selected model."""
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if self.model_name == "whisper":
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# # Whisper expects float32 data
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# # Convert int16 PCM back to float32
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# audio_np = np.frombuffer(audio_data, np.int16).astype(np.float32) / 32768.0
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# # Transcribe using Whisper model
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# segments, _ = self.whisper_model.transcribe(audio_np, beam_size=5)
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# transcription = " ".join([segment.text for segment in segments])
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# print(f"Whisper Transcription: {transcription}")
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# return transcription
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# Whisper expects float32 data
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energy_threshold=0.001
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audio_np = np.frombuffer(audio_data, np.int16).astype(np.float32) / 32768.0
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# Calculate energy of the audio to determine if it should be transcribed
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energy = np.mean(np.abs(audio_np))
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# Only transcribe if energy exceeds the threshold
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if energy > energy_threshold:
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# print(f"Audio energy ({energy}) exceeds threshold ({energy_threshold}), proceeding with transcription.")
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segments, _ = self.whisper_model.transcribe(audio_np, beam_size=5)
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transcription = " ".join([segment.text for segment in segments])
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print(f"Whisper Transcription: {transcription}")
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return transcription
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else:
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# print(f"Audio energy ({energy}) is below the threshold ({energy_threshold}), skipping transcription.")
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return ""
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elif self.model_name == "vosk":
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# Convert audio data to bytes for Vosk
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if self.recognizer.AcceptWaveform(audio_data):
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result = json.loads(self.recognizer.Result())
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if result["text"]:
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print(f"Recognized: {result['text']}")
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return result['text']
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return result
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elif self.model_name == "whisper":
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result, _ = self.recognizer.transcribe(audio_data, beam_size=5)
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return result['text']
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result = self.recognizer.Result()
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print(f"Vosk Transcription: {result}")
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return result
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else:
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result = self.recognizer.recognize_google(audio_data)
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return result
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# Fallback to default recognizer (for example, speech_recognition module)
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recognizer = sr.Recognizer()
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with sr.AudioFile(audio_data) as source:
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audio = recognizer.record(source)
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try:
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transcription = recognizer.recognize_google(audio)
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print(f"Google Transcription: {transcription}")
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return transcription
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except sr.UnknownValueError:
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print("Google could not understand audio")
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except sr.RequestError as e:
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print(f"Could not request results; {e}")
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def listen(self, time_listen=8):
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"""Main transcoder function that handles listening, noise cancellation, and transcription."""
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# Listen to the microphone and get both raw audio and ambient noise
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raw_audio, ambient_noise = self.listen_to_microphone(time_listen)
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|
||||
# def vosk_transcription(self):
|
||||
# """
|
||||
# Handles Vosk-based transcription of streamed audio with noise cancellation.
|
||||
# """
|
||||
# recognizer = KaldiRecognizer(self.vosk_model, self.sample_rate)
|
||||
# stream = self.stream_with_noise_cancellation()
|
||||
# Apply noise cancellation using the ambient noise from the first 2 seconds
|
||||
clean_audio = self.apply_noise_cancellation(raw_audio, ambient_noise=ambient_noise)
|
||||
|
||||
# for audio_chunk in stream:
|
||||
# if recognizer.AcceptWaveform(audio_chunk):
|
||||
# result = recognizer.Result()
|
||||
# print(result) # Handle or process the transcription result
|
||||
# Transcribe the clean audio
|
||||
transcription = self.transcribe(clean_audio)
|
||||
|
||||
# def whisper_transcription(self):
|
||||
# """
|
||||
# Handles Faster-Whisper-based transcription of streamed audio with noise cancellation.
|
||||
# """
|
||||
# stream = self.stream_with_noise_cancellation()
|
||||
|
||||
# for audio_chunk in stream:
|
||||
# # Transcribe the cleaned audio using faster-whisper
|
||||
# result, _ = self.whisper_model.transcribe(audio_chunk, beam_size=5)
|
||||
# print(result['text']) # Handle or process the transcription result
|
||||
|
||||
# def listen(self):
|
||||
# if self.model == "vosk":
|
||||
# self.vosk_transcription()
|
||||
# elif self.model == "whisper":
|
||||
# self.whisper_transcription()
|
||||
# else:
|
||||
# raise ValueError("Invalid model specified. Please specify either 'vosk' or 'whisper'.")
|
||||
return transcription
|
||||
|
||||
def glitch_stream_output(self, text):
|
||||
def change_pitch(sound, octaves):
|
||||
@ -209,10 +220,8 @@ class Speak:
|
||||
except:
|
||||
self.engine.say(text)
|
||||
self.engine.runAndWait()
|
||||
|
||||
# Example usage:
|
||||
# sp = Speak(vosk_model_path="path_to_vosk_model", whisper_model_path="large-v2")
|
||||
# sp.vosk_transcription() # To start Vosk transcription
|
||||
# sp.whisper_transcription() # To start Faster-Whisper transcription
|
||||
sp = Speak()
|
||||
# sp.glitch_stream_output("Hello, world!")
|
||||
sp.listen3()
|
||||
# sp = Speak(model="vosk") # or "vosk" or "google"
|
||||
# transcription = sp.transcoder(time_listen=10)
|
||||
# print("Final Transcription:", transcription)
|
||||
|
||||
@ -89,7 +89,7 @@ class Speak:
|
||||
# return result['text']
|
||||
# else:
|
||||
# print(f"Ambient noise detected: RMS {rms_value} exceeds threshold {noise_threshold}")
|
||||
# count += 1
|
||||
#
|
||||
# except KeyboardInterrupt:
|
||||
# print("Stopping...")
|
||||
# finally:
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user