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try:
from ..llm import get_client
from ..utils.db import *
from .stt_providers.openai import stt_openai
from .stt_providers.openai_whisper_local import stt_openai_whisper_local
except ImportError:
from utils.db import *
from audio.stt_providers.openai import stt_openai
from audio.stt_providers.openai_whisper_local import stt_openai_whisper_local
import os
from pydub import AudioSegment
def is_local_stt_available():
try:
return True
except:
return False
def split_audio(file_path, max_size=20 * 1024 * 1024):
"""Split an audio file into smaller parts if it exceeds a maximum size.
Args:
file_path (str): The path to the audio file to be split.
max_size (int): The maximum size in bytes for each split part. Defaults to 20 MB.
Returns:
list: A list of tuples containing the split audio segments and their respective file paths.
"""
audio = AudioSegment.from_wav(file_path)
file_size = os.path.getsize(file_path)
if file_size <= max_size:
return [(audio, file_path)]
# Calculate the number of parts needed
num_parts = file_size // max_size + 1
part_length = len(audio) // num_parts
parts = []
for i in range(num_parts):
start = i * part_length
end = (i + 1) * part_length if (i + 1) < num_parts else len(audio)
part = audio[start:end]
part_path = f"{file_path[:-4]}_part_{i+1}.wav"
part.export(part_path, format="wav")
parts.append((part, part_path))
return parts
def speech_to_text(location):
"""Convert speech audio file to text using an external service.
Args:
location (str): The path to the speech audio file.
Returns:
str: The transcribed text from the speech audio file.
"""
audio_parts = split_audio(location)
transcriptions = []
for part, part_path in audio_parts:
with open(part_path, "rb") as audio_file:
if load_stt_model_settings() == "openai":
transcription = stt_openai(audio_file)
else:
transcription = stt_openai_whisper_local(part_path)
transcriptions.append(transcription)
os.remove(part_path) # Clean up the temporary file immediately after processing
# Merge transcriptions (assuming it's a list of text segments)
full_transcription = " ".join(transcription for transcription in transcriptions)
return full_transcription
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