mirror of
https://github.com/patw/AudioSumma.git
synced 2025-04-16 13:59:08 +00:00
115 lines
No EOL
3.9 KiB
Python
115 lines
No EOL
3.9 KiB
Python
import os
|
|
import requests
|
|
import datetime
|
|
from dotenv import load_dotenv
|
|
|
|
# Load environment variables from .env file
|
|
load_dotenv()
|
|
|
|
# Load settings from environment
|
|
WHISPERCPP_URL = os.getenv("WHISPERCPP_URL")
|
|
LLAMACPP_URL = os.getenv("LLAMACPP_URL")
|
|
SYSTEM_MESSAGE = os.getenv("SYSTEM_MESSAGE")
|
|
SUMMARY_PROMPT = os.getenv("SUMMARY_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"))
|
|
TEMPERATURE = float(os.getenv("TEMPERATURE"))
|
|
|
|
def whisper_api(file):
|
|
# Whisper supports multiple files, but we're sending one
|
|
files = {"file": file}
|
|
|
|
# Required API call data
|
|
api_data = {
|
|
"temperature": "0.0",
|
|
"response_format": "json"
|
|
}
|
|
|
|
# Call API and return text
|
|
response = requests.post(WHISPERCPP_URL, data=api_data, files=files)
|
|
return response.json()["text"]
|
|
|
|
def llama_api(prompt):
|
|
# Format prompt before sending
|
|
formatted_prompt = PROMPT_FORMAT.format(system=SYSTEM_MESSAGE, prompt=prompt)
|
|
|
|
api_data = {
|
|
"prompt": formatted_prompt,
|
|
"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']
|
|
|
|
# Iterate over each WAV file and transcode with whisper API
|
|
wav_files = [f for f in os.listdir(".") if f.endswith(".wav")]
|
|
for wav_file in wav_files:
|
|
# Open the WAV file
|
|
with open(wav_file, "rb") as file:
|
|
print("Transcribing: " + wav_file)
|
|
# Call whisper API to transcode file
|
|
output_text = whisper_api(file)
|
|
|
|
# Generate the output file name by replacing the extension with .tns
|
|
output_file = os.path.splitext(wav_file)[0] + ".tns"
|
|
|
|
# Write the output text to the file
|
|
with open(output_file, "w") as output:
|
|
output.write(output_text)
|
|
|
|
# Chunk the full transcript into multiple parts to fit in the context window
|
|
# and allow for better reasoning capability
|
|
def chunk_transcript(string, chunk_size):
|
|
chunks = []
|
|
lines = string.split("\n") # Split the string on newline characters
|
|
current_chunk = ""
|
|
for line in lines:
|
|
current_chunk += line # Build up the string until the chunk size is reached
|
|
if len(current_chunk) >= chunk_size:
|
|
chunks.append(current_chunk)
|
|
current_chunk = ""
|
|
if current_chunk: # Add the last chunk if it's not empty
|
|
chunks.append(current_chunk)
|
|
return chunks
|
|
|
|
# Get the current date in yyyymmdd format
|
|
today = datetime.datetime.now().strftime('%Y%m%d')
|
|
|
|
# Modify the filename by appending the current date
|
|
summary_filename = "summary-" + today + ".md"
|
|
|
|
# Get the list of transcript files in the current directory
|
|
transcript_files = [f for f in os.listdir(".") if f.endswith(".tns")]
|
|
|
|
# Iterate over each WAV file
|
|
for transcript in transcript_files:
|
|
print("Summarizing: " + transcript)
|
|
|
|
# Open the WAV file
|
|
with open(transcript, "r") as file:
|
|
transcript_data = file.read()
|
|
|
|
# chunk the transcript so we don't blow out the context window
|
|
chunked_data = chunk_transcript(transcript_data, CHUNK_SIZE)
|
|
|
|
# Iterate through the chunks, and summarize them
|
|
for i, chunk in enumerate(chunked_data):
|
|
with open(summary_filename, "a") as md_file:
|
|
# Generate call summary
|
|
summary_prompt = SUMMARY_PROMPT.format(chunk=chunk)
|
|
summary = llama_api(summary_prompt)
|
|
|
|
# Generate call sentiment
|
|
sentiment_prompt = SENTIMENT_PROMPT.format(chunk=chunk)
|
|
sentiment = llama_api(sentiment_prompt)
|
|
|
|
# Write the notes
|
|
md_file.write(f"# Summary - {transcript} - Part {i + 1}\n\n{summary}\n\n{sentiment}\n\n---\n")
|
|
|
|
print("Summarizing complete") |