mirror of
https://github.com/patw/AudioSumma.git
synced 2025-04-13 20:39:09 +00:00
119 lines
No EOL
4.3 KiB
Python
119 lines
No EOL
4.3 KiB
Python
import os
|
|
import requests
|
|
import datetime
|
|
import tempfile
|
|
import subprocess
|
|
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_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")
|
|
FACT_PROMPT = os.getenv("FACT_PROMPT")
|
|
SENTIMENT_PROMPT = os.getenv("SENTIMENT_PROMPT")
|
|
CHUNK_SIZE = int(os.getenv("CHUNK_SIZE"))
|
|
TEMPERATURE = float(os.getenv("TEMPERATURE"))
|
|
TOP_P = float(os.getenv("TOP_P"))
|
|
MAX_TOKENS = float(os.getenv("MAX_TOKENS"))
|
|
|
|
def whisper_api(file):
|
|
"""Transcribe audio file using Whisper API."""
|
|
files = {"file": file}
|
|
api_data = {
|
|
"temperature": "0.0",
|
|
"response_format": "json"
|
|
}
|
|
response = requests.post(WHISPERCPP_URL, data=api_data, files=files)
|
|
return response.json()["text"]
|
|
|
|
def llm_local(prompt):
|
|
client = OpenAI(api_key="doesntmatter", base_url=LLAMACPP_URL)
|
|
messages=[{"role": "system", "content": SYSTEM_MESSAGE},{"role": "user", "content": prompt}]
|
|
response = client.chat.completions.create(model="whatever", max_tokens=MAX_TOKENS, temperature=TEMPERATURE, top_p=TOP_P, messages=messages)
|
|
return response.choices[0].message.content
|
|
|
|
def trim_silence(filename):
|
|
"""Trim silence from audio file using FFmpeg."""
|
|
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file:
|
|
temp_filename = temp_file.name
|
|
|
|
ffmpeg_command = [
|
|
"ffmpeg",
|
|
"-i", filename,
|
|
"-af", "silenceremove=stop_threshold=-40dB:stop_duration=1:stop_periods=-1",
|
|
"-y", # Overwrite output file if it exists
|
|
temp_filename
|
|
]
|
|
|
|
result = subprocess.run(ffmpeg_command, capture_output=True, text=True, check=True)
|
|
os.replace(temp_filename, filename)
|
|
|
|
def process_wav_files():
|
|
"""Process WAV files: trim silence and transcribe."""
|
|
wav_files = [f for f in os.listdir(".") if f.endswith(".wav")]
|
|
for wav_file in wav_files:
|
|
# Generate the expected transcript filename
|
|
transcript_file = os.path.splitext(wav_file)[0] + ".tns"
|
|
|
|
# Check if transcript already exists
|
|
if os.path.exists(transcript_file):
|
|
print(f"Transcript already exists for {wav_file}, skipping transcription")
|
|
continue
|
|
|
|
print("Trimming silence: " + wav_file)
|
|
trim_silence(wav_file)
|
|
|
|
with open(wav_file, "rb") as file:
|
|
print("Transcribing: " + wav_file)
|
|
output_text = whisper_api(file)
|
|
output_file = os.path.splitext(wav_file)[0] + ".tns"
|
|
with open(output_file, "w") as output:
|
|
output.write(output_text)
|
|
|
|
def chunk_transcript(string, chunk_size):
|
|
"""Chunk the transcript to fit in the context window."""
|
|
chunks = []
|
|
lines = string.split("\n")
|
|
current_chunk = ""
|
|
for line in lines:
|
|
current_chunk += line
|
|
if len(current_chunk) >= chunk_size:
|
|
chunks.append(current_chunk)
|
|
current_chunk = ""
|
|
if current_chunk:
|
|
chunks.append(current_chunk)
|
|
return chunks
|
|
|
|
def summarize_transcripts():
|
|
"""Summarize transcript files."""
|
|
today = datetime.datetime.now().strftime('%Y%m%d')
|
|
summary_filename = "summary-" + today + ".md"
|
|
transcript_files = [f for f in os.listdir(".") if f.endswith(".tns")]
|
|
|
|
for transcript in transcript_files:
|
|
print("Summarizing: " + transcript)
|
|
with open(transcript, "r") as file:
|
|
transcript_data = file.read()
|
|
chunked_data = chunk_transcript(transcript_data, CHUNK_SIZE)
|
|
|
|
with open(summary_filename, "a") as md_file:
|
|
for i, chunk in enumerate(chunked_data):
|
|
print("Processing part " + str(i))
|
|
summary = llm_local(SUMMARY_PROMPT.format(chunk=chunk))
|
|
facts = llm_local(FACT_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")
|
|
|
|
print("Summarizing complete")
|
|
|
|
if __name__ == "__main__":
|
|
process_wav_files()
|
|
summarize_transcripts() |