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Simplified the transcriber by using the openai library to access llama.cpp server or ollama server mode.

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Pat Wendorf 2024-11-29 09:06:22 -05:00
parent 77b7f440a0
commit 80637cc26c
2 changed files with 15 additions and 19 deletions
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5
requirements.txt
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@ -7,4 +7,7 @@ pyaudio
# Transcriber # Transcriber
requests requests
dotenv dotenv
# LLM Interface
openai

29
summarize.py
View file

@ -5,6 +5,9 @@ import tempfile
import subprocess import subprocess
from dotenv import load_dotenv from dotenv import load_dotenv
# Use local models with the OpenAI library and a custom baseurl
from openai import OpenAI
# Load environment variables from .env file # Load environment variables from .env file
load_dotenv() load_dotenv()
@ -15,8 +18,6 @@ SYSTEM_MESSAGE = os.getenv("SYSTEM_MESSAGE")
SUMMARY_PROMPT = os.getenv("SUMMARY_PROMPT") SUMMARY_PROMPT = os.getenv("SUMMARY_PROMPT")
FACT_PROMPT = os.getenv("FACT_PROMPT") FACT_PROMPT = os.getenv("FACT_PROMPT")
SENTIMENT_PROMPT = os.getenv("SENTIMENT_PROMPT") SENTIMENT_PROMPT = os.getenv("SENTIMENT_PROMPT")
PROMPT_FORMAT = os.getenv("PROMPT_FORMAT")
STOP_TOKEN = os.getenv("STOP_TOKEN")
CHUNK_SIZE = int(os.getenv("CHUNK_SIZE")) CHUNK_SIZE = int(os.getenv("CHUNK_SIZE"))
TEMPERATURE = float(os.getenv("TEMPERATURE")) TEMPERATURE = float(os.getenv("TEMPERATURE"))
@ -30,19 +31,11 @@ def whisper_api(file):
response = requests.post(WHISPERCPP_URL, data=api_data, files=files) response = requests.post(WHISPERCPP_URL, data=api_data, files=files)
return response.json()["text"] return response.json()["text"]
def llama_api(prompt): def llm_local(prompt):
"""Generate response using llama.cpp server API.""" client = OpenAI(api_key="doesntmatter", base_url=LLAMACPP_URL)
formatted_prompt = PROMPT_FORMAT.format(system=SYSTEM_MESSAGE, prompt=prompt) messages=[{"role": "system", "content": SYSTEM_MESSAGE},{"role": "user", "content": prompt}]
api_data = { response = client.chat.completions.create(model="whatever", temperature=TEMPERATURE, messages=messages)
"prompt": formatted_prompt, return response.choices[0].message.content
"n_predict": -1,
"temperature": TEMPERATURE,
"stop": [STOP_TOKEN],
"tokens_cached": 0
}
response = requests.post(LLAMACPP_URL, headers={"Content-Type": "application/json"}, json=api_data)
json_output = response.json()
return json_output['content']
def trim_silence(filename): def trim_silence(filename):
"""Trim silence from audio file using FFmpeg.""" """Trim silence from audio file using FFmpeg."""
@ -102,9 +95,9 @@ def summarize_transcripts():
with open(summary_filename, "a") as md_file: with open(summary_filename, "a") as md_file:
for i, chunk in enumerate(chunked_data): for i, chunk in enumerate(chunked_data):
summary = llama_api(SUMMARY_PROMPT.format(chunk=chunk)) summary = llm_local(SUMMARY_PROMPT.format(chunk=chunk))
facts = llama_api(FACT_PROMPT.format(chunk=chunk)) facts = llm_local(FACT_PROMPT.format(chunk=chunk))
sentiment = llama_api(SENTIMENT_PROMPT.format(chunk=chunk)) sentiment = llm_local(SENTIMENT_PROMPT.format(chunk=chunk))
md_file.write(f"# Call Transcript - {transcript} - Part {i + 1}\n\nSummary: {summary}\n\nFacts:\n{facts}\n\nSentiment: {sentiment}\n\n---\n") md_file.write(f"# Call Transcript - {transcript} - Part {i + 1}\n\nSummary: {summary}\n\nFacts:\n{facts}\n\nSentiment: {sentiment}\n\n---\n")