Add distilled whisper-medium model

I converted distil-whisper-medium.en to CTranslate2 format and uploaded
it to huggingface. This model is exceptionally fast and light compared
to the non-distilled version, at the cost of some accuracy.

1 files changed, 17 insertions, 3 deletions

diff --git a/Scripts/transcribe_v2.py b/Scripts/transcribe_v2.py
index 889e1cf..4ae17d1 100644
--- a/Scripts/transcribe_v2.py
+++ b/Scripts/transcribe_v2.py
@@ -3,6 +3,7 @@ from datetime import datetime
 from emotes_v2 import EmotesState
 from faster_whisper import WhisperModel
 from functools import partial
+from huggingface_hub import hf_hub_download
 from profanity_filter import ProfanityFilter
 from pydub import AudioSegment
 from sentence_splitter import split_text_into_sentences
@@ -410,7 +411,8 @@ class Whisper:
         my_dir = os.path.dirname(abspath)
         parent_dir = os.path.dirname(my_dir)
 
-        model_root = os.path.join(parent_dir, "Models", cfg["model"])
+        model_str = cfg["model"]
+        model_root = os.path.join(parent_dir, "Models", model_str)
         print(f"Model {cfg['model']} will be saved to {model_root}",
                 file=sys.stderr)
 
@@ -419,13 +421,21 @@ class Whisper:
             model_device = "cpu"
 
         download_it = os.path.exists(model_root)
-        model_str = cfg["model"]
+        model_str = model_str
         if download_it:
+            if '/' in model_str:
+                hf_hub_download(repo_id=model_str, filename='model.bin',
+                        local_dir=model_root)
+                hf_hub_download(repo_id=model_str, filename='vocabulary.json',
+                        local_dir=model_root)
+                hf_hub_download(repo_id=model_str, filename='config.json',
+                        local_dir=model_root)
+
             model_str = model_root
         self.model = WhisperModel(model_str,
                 device = model_device,
                 device_index = cfg["gpu_idx"],
-                compute_type = "int8",
+                compute_type = "float16",
                 download_root = model_root,
                 local_files_only = download_it)
 
@@ -437,6 +447,7 @@ class Whisper:
         audio = np.frombuffer(frames,
                 dtype=np.int16).flatten().astype(np.float32) / 32768.0
 
+        t0 = time.time()
         segments, info = self.model.transcribe(
                 audio,
                 language = langcodes.find(self.cfg["language"]).language,
@@ -458,6 +469,9 @@ class Whisper:
             res.append(Segment(s.text, s.start, s.end,
                 self.collector.begin(),
                 s.avg_logprob, s.no_speech_prob, s.compression_ratio))
+        t1 = time.time()
+        if cfg["enable_debug_mode"]:
+            print(f"Transcription latency (s): {t1 - t0}")
         return res
 
 def saveAudio(audio: bytes, path: str):