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import os
import csv
from tqdm import tqdm # For progress bar display
from funasr import AutoModel
from funasr.utils.postprocess_utils import rich_transcription_postprocess
# Define model path
model_dir = "iic/SenseVoiceSmall"
# Initialize model
model = AutoModel(
model=model_dir,
trust_remote_code=True,
remote_code="./model.py",
vad_model="fsmn-vad",
vad_kwargs={"max_single_segment_time": 30000},
device="cuda:0",
)
# Define audio folder path
audio_folder = ""
# Output CSV file path
output_csv = "./recognition_results.csv"
# Get all .flac files in audio folder
audio_files = [f for f in os.listdir(audio_folder) if f.endswith(".flac")]
# Prepare CSV file and write header (if file is empty)
if not os.path.exists(output_csv) or os.path.getsize(output_csv) == 0:
with open(output_csv, mode="w", newline="", encoding="utf-8") as file:
writer = csv.writer(file)
writer.writerow(["Audio File", "Transcription"]) # CSV column headers
# Get existing processed audio files to avoid reprocessing
existing_files = set()
with open(output_csv, mode="r", newline="", encoding="utf-8") as file:
reader = csv.reader(file)
next(reader) # Skip header row
for row in reader:
existing_files.add(row[0]) # Add processed files to set
# Process all .flac files in audio folder
with open(output_csv, mode="a", newline="", encoding="utf-8") as file:
writer = csv.writer(file)
# Show progress bar using tqdm
for audio_file in tqdm(audio_files, desc="Processing", unit="file"):
# Skip if file already processed
if audio_file in existing_files:
continue
audio_path = os.path.join(audio_folder, audio_file)
try:
# Perform speech recognition
res = model.generate(
input=audio_path,
cache={},
language="auto", # Auto-detect language
use_itn=True,
batch_size_s=60,
merge_vad=True,
merge_length_s=15,
)
# Get transcription with post-processing
transcription = rich_transcription_postprocess(res[0]["text"])
# Mark as "none!" if transcription is empty
if not transcription.strip():
transcription = "none!"
except Exception as e:
# Record error if recognition fails
transcription = f"Error: {str(e)}"
# Write filename and transcription to CSV
writer.writerow([audio_file, transcription])
print("Recognition completed and saved to CSV.") |