Spaces:
Running
Running
File size: 1,830 Bytes
53978c9 |
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 |
import os
from pydub import AudioSegment
import whisper
from deep_translator import GoogleTranslator
# @title Audio into chunks
def audio_into_chunks_transcribe_translate(audio_file,lang):
chunk_length_seconds=11
output_format="wav"
# Check if file exists
if not os.path.exists(audio_file):
raise ValueError(f"FLAC file not found: {audio_file}")
Transcribe_Text=[]
# Load the FLAC audio
audio_segment = AudioSegment.from_file(audio_file, format="flac")
#load Model For Transcribe
model = whisper.load_model("medium")
# Get total audio duration in milliseconds
total_duration_ms = audio_segment.duration_seconds * 1000
# Calculate chunk duration in milliseconds
chunk_duration_ms = chunk_length_seconds * 1000
# Split audio into chunks
start_time = 0
chunk_num = 1
while start_time < total_duration_ms:
# Get the end time for the current chunk
end_time = min(start_time + chunk_duration_ms, total_duration_ms)
# Extract the current chunk
chunk = audio_segment[start_time:end_time]
# Generate output filename with sequential numbering
output_filename = f"{os.path.splitext(os.path.basename(audio_file))[0]}_chunk_{chunk_num}.{output_format}"
# Export the chunk as the specified format
chunk.export(output_filename, format=output_format)
# Update start time for the next chunk
start_time += chunk_duration_ms
chunk_num += 1
#transcribe Chunks
result = model.transcribe(output_filename)
#translate the transcribe data
translator=GoogleTranslator(source='auto',target=lang)
data_trans=translator.translate(result['text'])
Transcribe_Text.append(data_trans)
print(data_trans)
print(result['text'])
return Transcribe_Text
print(f"FLAC file '{flac_filepath}' successfully split into {chunk_num - 1} chunks.")
|