diff --git a/download_model.py b/download_model.py
index 9c96384..fda2716 100644
--- a/download_model.py
+++ b/download_model.py
@@ -1,3 +1,3 @@
 from google.colab import files
 files.download("./OUTPUT_MODEL/G_latest.pth")
-files.download("./configs/modified_finetune_speaker.json")
\ No newline at end of file
+files.download("./finetune_speaker.json")
\ No newline at end of file
diff --git a/video_transcribe.py b/video_transcribe.py
new file mode 100644
index 0000000..c820d8a
--- /dev/null
+++ b/video_transcribe.py
@@ -0,0 +1,43 @@
+from moviepy.editor import AudioFileClip
+import whisper
+import os
+import torchaudio
+parent_dir = "../"
+filelist = ["taffy1.mp4", "taffy2.mp4"]
+for file in filelist:
+    my_audio_clip = AudioFileClip(parent_dir + file)
+    my_audio_clip.write_audiofile(parent_dir + file.rstrip(".mp4") + ".wav")
+for file in filelist:
+    file = file.replace(".mp4", ".wav")
+    os.system(f"demucs --two-stems=vocals {parent_dir}{file}")
+for file in filelist:
+    file = file.strip(".mp4")
+    wav, sr = torchaudio.load(f"./separated/htdemucs/{file}/vocals.wav", frame_offset=0, num_frames=-1, normalize=True,
+                              channels_first=True)
+    # merge two channels into one
+    wav = wav.mean(dim=0).unsqueeze(0)
+    if sr != 22050:
+        wav = torchaudio.transforms.Resample(orig_freq=sr, new_freq=22050)(wav)
+    torchaudio.save(file + ".wav", wav, 22050, channels_first=True)
+model = whisper.load_model("medium")
+def transcribe_one(audio_path):
+    # load audio and pad/trim it to fit 30 seconds
+    audio = whisper.load_audio(audio_path)
+    audio = whisper.pad_or_trim(audio)
+
+    # make log-Mel spectrogram and move to the same device as the model
+    mel = whisper.log_mel_spectrogram(audio).to(model.device)
+
+    # detect the spoken language
+    _, probs = model.detect_language(mel)
+    lang = max(probs, key=probs.get)
+    # decode the audio
+    options = whisper.DecodingOptions()
+    result = whisper.decode(model, mel, options)
+
+    # print the recognized text
+    return result
+
+result = model.transcribe("taffy2.wav")
+# segment audio based on segment results
+