File size: 7,804 Bytes
20d720d b28635c 7a4afbb a69210a 3019028 b28635c 6d99a60 b73fa18 0f20f6a 96970ff 0f20f6a 7a4afbb 0f20f6a 7a4afbb 20d720d 7a4afbb 20d720d 7a4afbb 0f20f6a c537b15 7a4afbb d39c478 3019028 20d720d 7a4afbb 3019028 0f20f6a 20d720d c537b15 7a4afbb d39c478 c537b15 7a4afbb 0f20f6a 292f6f6 7a4afbb 20d720d 7a4afbb 20d720d c537b15 7a4afbb d39c478 c537b15 7a4afbb 0f20f6a 20d720d 7a4afbb 20d720d 7a4afbb c537b15 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 |
import numpy as np
import asyncio
#from .base_tool import BaseTool
from models.tinygpt2_model import TinyGPT2Model
from transformers import pipeline, AutoProcessor, AutoModelForSpeechSeq2Seq
import os
import tempfile
import soundfile as sf
import torch
from pydantic import PrivateAttr
from crewai.tools import BaseTool
# class MultilingualVoiceProcessor:
# def __init__(self, model_name="openai/whisper-base", device=None):
# cache_dir = os.getenv("TRANSFORMERS_CACHE", None)
# if device is None:
# device = 0 if torch.cuda.is_available() else -1
# # Load model and processor with cache_dir
# processor = AutoProcessor.from_pretrained(model_name, cache_dir=cache_dir)
# model = AutoModelForSpeechSeq2Seq.from_pretrained(model_name, cache_dir=cache_dir)
# # Create the pipeline, DO NOT PASS cache_dir here
# # self.pipe = pipeline(
# # "automatic-speech-recognition",
# # model=model,
# # tokenizer=processor,
# # feature_extractor=processor,
# # device=device,
# # generate_kwargs={"task": "transcribe", "return_timestamps": False},
# # )
# self.pipe = pipeline(
# "automatic-speech-recognition",
# model=model_name,
# device=device,
# generate_kwargs={"task": "transcribe", "return_timestamps": False},
# )
# async def transcribe(self, audio_data: np.ndarray, language: str = None):
# with tempfile.NamedTemporaryFile(suffix=".wav", delete=True) as tmp_wav:
# sf.write(tmp_wav.name, audio_data, samplerate=16000)
# extra = {"language": language} if language else {}
# result = self.pipe(tmp_wav.name, **extra)
# text = result['text']
# return text, language or "unknown"
# async def synthesize(self, text, language: str = "en", voice_type: str = "normal"):
# raise NotImplementedError("Use gTTS or edge-tts as before.")
# class TranscribeAudioTool(BaseTool):
# name: str = "transcribe_audio"
# description: str = "Transcribe audio to text and detect language."
# model_config = {"arbitrary_types_allowed": True}
# #_vp: MultilingualVoiceProcessor = PrivateAttr()
# def __init__(self, config=None):
# super().__init__()
# self.vp = MultilingualVoiceProcessor()
# def _run(self, audio_data: np.ndarray, language=None):
# text, detected_lang = asyncio.run(self.vp.transcribe(audio_data, language))
# return {"text": text, "language": detected_lang}
# class DetectEmotionTool(BaseTool):
# name: str = "detect_emotion"
# description: str = "Detect the emotional state from text."
# model_config = {"arbitrary_types_allowed": True}
# def __init__(self, config=None):
# super().__init__()
# def _run(self, text: str):
# model = TinyGPT2Model()
# prompt = f'Analyse emotions in: "{text}". Format: JSON with primary_emotion, intensity, feelings, concerns.'
# response = model.generate(prompt)
# return {"primary_emotion": "detected_emotion",
# "intensity": "medium",
# "feelings": ["feeling1"],
# "concerns": ["concern1"]}
# class GenerateReflectiveQuestionsTool(BaseTool):
# name: str = "generate_reflective_questions"
# description: str = "Generate reflective questions."
# model_config = {"arbitrary_types_allowed": True}
# def __init__(self, config=None):
# super().__init__()
# def _run(self, context: dict):
# emotion = context.get("primary_emotion", "neutral")
# questions_map = {
# "anxiety": ["What triggers your anxiety?", "How do you cope?"],
# "sadness": ["What helps when you feel sad?", "Who can you talk to?"]
# }
# return questions_map.get(emotion, [
# "How are you feeling?",
# "What feels important now?"
# ])
# class VoiceTools:
# def __init__(self, config=None):
# self.transcribe_audio = TranscribeAudioTool(config)
# self.detect_emotion = DetectEmotionTool(config)
# self.generate_reflective_questions = GenerateReflectiveQuestionsTool(config)
import numpy as np
import asyncio
from typing import List, Optional
from models.tinygpt2_model import TinyGPT2Model
from transformers import pipeline, AutoProcessor, AutoModelForSpeechSeq2Seq
import os
import tempfile
import soundfile as sf
import torch
from crewai.tools import BaseTool
class MultilingualVoiceProcessor:
def __init__(self, model_name="openai/whisper-base", device=None):
cache_dir = os.getenv("TRANSFORMERS_CACHE", None)
if device is None:
device = 0 if torch.cuda.is_available() else -1
self.pipe = pipeline(
"automatic-speech-recognition",
model=model_name,
device=device,
generate_kwargs={"task": "transcribe", "return_timestamps": False},
)
async def transcribe(self, audio_data: np.ndarray, language: str = None):
with tempfile.NamedTemporaryFile(suffix=".wav", delete=True) as tmp_wav:
sf.write(tmp_wav.name, audio_data, samplerate=16000)
extra = {"language": language} if language else {}
result = self.pipe(tmp_wav.name, **extra)
text = result['text']
return text, language or "unknown"
async def synthesize(self, text, language: str = "en", voice_type: str = "normal"):
raise NotImplementedError("Use gTTS or edge-tts as before.")
class TranscribeAudioTool(BaseTool):
name: str = "transcribe_audio"
description: str = "Transcribe audio to text and detect language."
model_config = {"arbitrary_types_allowed": True}
_vp: MultilingualVoiceProcessor = PrivateAttr()
def __init__(self, config=None):
super().__init__()
self._vp = MultilingualVoiceProcessor()
def _run(self, audio_data: List[float], language: Optional[str] = None):
audio_np = np.array(audio_data, dtype=np.float32)
text, detected_lang = asyncio.run(self.vp.transcribe(audio_np, language))
return {"text": text, "language": detected_lang}
class DetectEmotionTool(BaseTool):
name: str = "detect_emotion"
description: str = "Detect the emotional state from text."
model_config = {"arbitrary_types_allowed": True}
def __init__(self, config=None):
super().__init__()
def _run(self, text: str):
model = TinyGPT2Model()
prompt = f'Analyse emotions in: "{text}". Format: JSON with primary_emotion, intensity, feelings, concerns.'
response = model.generate(prompt)
return {"primary_emotion": "detected_emotion",
"intensity": "medium",
"feelings": ["feeling1"],
"concerns": ["concern1"]}
class GenerateReflectiveQuestionsTool(BaseTool):
name: str = "generate_reflective_questions"
description: str = "Generate reflective questions."
model_config = {"arbitrary_types_allowed": True}
def __init__(self, config=None):
super().__init__()
def _run(self, context: dict):
emotion = context.get("primary_emotion", "neutral")
questions_map = {
"anxiety": ["What triggers your anxiety?", "How do you cope?"],
"sadness": ["What helps when you feel sad?", "Who can you talk to?"]
}
return questions_map.get(emotion, [
"How are you feeling?",
"What feels important now?"
])
class VoiceTools:
def __init__(self, config=None):
self.transcribe_audio = TranscribeAudioTool(config)
self.detect_emotion = DetectEmotionTool(config)
self.generate_reflective_questions = GenerateReflectiveQuestionsTool(config) |