remove necessity for file cache

Author: Flux9665

run_asvspoof_generation.py

@@ -2,26 +2,42 @@
 
 import librosa
 import soundfile as sf
-import torch
 from tqdm import tqdm
 
 from InferenceInterfaces.ToucanTTSInterface import ToucanTTSInterface
 from Utility.utils import float2pcm
 
+PATH_TO_MLS_ENGLISH_TRAIN = "/mount/resources/speech/corpora/MultiLingLibriSpeech/mls_english/train"
 PATH_TO_GENERATION_FILE = "p1_ttsvc_surrogate.tsv"
 PATH_TO_OUTPUT_DIR = "asv_spoof_outputs_no_pros"
 DEVICE = "cuda"
 
+
+def build_path_to_transcript_dict_mls_english():
+    path_to_transcript = dict()
+    with open(os.path.join(PATH_TO_MLS_ENGLISH_TRAIN, "transcripts.txt"), "r", encoding="utf8") as file:
+        lookup = file.read()
+    for line in lookup.split("\n"):
+        if line.strip() != "":
+            fields = line.split("\t")
+            wav_folders = fields[0].split("_")
+            wav_path = f"{PATH_TO_MLS_ENGLISH_TRAIN}/audio/{wav_folders[0]}/{wav_folders[1]}/{fields[0]}.flac"
+            path_to_transcript[wav_path] = fields[1]
+    return path_to_transcript
+
+
 if __name__ == '__main__':
+    print("loading model...")
     tts = ToucanTTSInterface(device=DEVICE, tts_model_path="ASVSpoof")
-    path_to_transcript_dict = torch.load("mls_transcript_cache.pt")
+    print("prepare path to transcript lookup...")
+    path_to_transcript_dict = build_path_to_transcript_dict_mls_english()
     filename_to_path = dict()
     for p in path_to_transcript_dict:
         filename_to_path[p.split("/")[-1].rstrip(".flac")] = p
     with open(PATH_TO_GENERATION_FILE, "r") as file:
         generation_list = file.read().split("\n")
     os.makedirs(PATH_TO_OUTPUT_DIR, exist_ok=True)
-
+    print("generating audios...")
     for generation_item in tqdm(generation_list):
         if generation_item == "":
             continue
@@ -34,7 +50,4 @@
         tts.set_utterance_embedding(path_to_reference_audio=source_list)
         cloned_utterance = tts(transcript)
         resampled_utt = librosa.resample(cloned_utterance, orig_sr=24000, target_sr=16000)
-        sf.write(file=f"{PATH_TO_OUTPUT_DIR}/" + output_name + ".flac",
-                 data=float2pcm(resampled_utt),
-                 samplerate=16000,
-                 subtype="PCM_16")
+        sf.write(file=f"{PATH_TO_OUTPUT_DIR}/" + output_name + ".flac", data=float2pcm(resampled_utt), samplerate=16000, subtype="PCM_16")

run_model_downloader.py

@@ -47,14 +47,6 @@ def download_models():
         filename=os.path.abspath(os.path.join(MODELS_DIR, "Embedding", "embedding_function.pt")),
         reporthook=report)
 
-    #############
-    print("Downloading Cleaned MLS Texts")
-    os.makedirs(os.path.join(MODELS_DIR, "Embedding"), exist_ok=True)
-    filename, headers = urllib.request.urlretrieve(
-        url="https://github.com/DigitalPhonetics/IMS-Toucan/releases/download/v2.asvspoof/mls_transcript_cache.pt",
-        filename="mls_transcript_cache.pt",
-        reporthook=report)
-
     #############
     print("Downloading Codec Model")
     filename, headers = urllib.request.urlretrieve(