Spaces:
Sleeping
Sleeping
File size: 2,623 Bytes
53d54bd 460899a 05d04ce 460899a 05d04ce 460899a 53d54bd 460899a 05d04ce 53d54bd 460899a 53d54bd |
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 |
import os
import pickle
from datetime import datetime
from pathlib import Path
from fish_audio_sdk import Session, TTSRequest
from openai import OpenAI
from tenacity import retry, stop_after_attempt, wait_exponential
INFO_audio_ID_dict = {
"matsu": os.getenv("INFO_audio_matsu"),
"ken": os.getenv("INFO_audio_ken"),
"mana": os.getenv("INFO_audio_mana"),
"cai": os.getenv("INFO_audio_cai"),
"ren": os.getenv("INFO_audio_ren"),
}
# print(INFO_audio_ID_dict)
client = OpenAI(api_key=os.getenv("gpt"))
#
audio_client = Session(os.getenv("audio"))
PKL_FILE = "./resource/knowledge_data.pkl"
def load_pkl(file_path):
if os.path.exists(file_path):
with open(file_path, "rb") as f:
return pickle.load(f)
else:
return {}
def save_pkl(file_name, data):
sorted_data = dict(
sorted(
data.items(),
key=lambda x: datetime.strptime("-".join(x[0].split("-")[0:3]), "%Y-%m-%d"),
)
)
with open(file_name, "wb") as f:
pickle.dump(sorted_data, f)
try:
from dotenv import load_dotenv
env_path = Path(os.path.dirname(__file__)) / ".env"
if env_path.exists():
load_dotenv(env_path)
except ImportError:
pass
# params
@retry(
stop=stop_after_attempt(5),
wait=wait_exponential(multiplier=1, min=2, max=10),
)
def generate_item(prompt, sys_prompt, model="gpt-4.1-mini", temperature=1.0):
# print("prompt=", prompt)
# print("temperature=", temperature)
response = client.chat.completions.create(
model=model,
temperature=temperature,
messages=[
{
"role": "system",
"content": sys_prompt,
},
{"role": "user", "content": prompt},
],
)
return response.choices[0].message.content.strip()
@retry(
stop=stop_after_attempt(5),
wait=wait_exponential(multiplier=1, min=2, max=10),
)
def get_embedding(text):
response = client.embeddings.create(input=text, model="text-embedding-3-small")
return [emb.embedding for emb in response.data]
@retry(
stop=stop_after_attempt(5),
wait=wait_exponential(multiplier=1, min=2, max=10),
)
def audio_text(text, audio_path, speaker_id):
# TODO change speaker
# print(speaker_id, ":", INFO_audio_ID_dict[speaker_id])
with open(audio_path, "wb") as f:
for chunk in audio_client.tts(
TTSRequest(
reference_id=INFO_audio_ID_dict[speaker_id], text=text, latency="normal"
),
backend="s1",
):
f.write(chunk)
return audio_path
|