small updates

bark/api.py

@@ -9,6 +9,7 @@ def text_to_semantic(
     text: str,
     history_prompt: Optional[str] = None,
     temp: float = 0.7,
+    silent: bool = False,
 ):
     """Generate semantic array from text.
 
@@ -16,6 +17,7 @@ def text_to_semantic(
         text: text to be turned into audio
         history_prompt: history choice for audio cloning
         temp: generation temperature (1.0 more diverse, 0.0 more conservative)
+        silent: disable progress bar
 
     Returns:
         numpy semantic array to be fed into `semantic_to_waveform`
@@ -24,6 +26,7 @@ def text_to_semantic(
         text,
         history_prompt=history_prompt,
         temp=temp,
+        silent=silent,
     )
     return x_semantic
 
@@ -32,6 +35,7 @@ def semantic_to_waveform(
     semantic_tokens: np.ndarray,
     history_prompt: Optional[str] = None,
     temp: float = 0.7,
+    silent: bool = False,
 ):
     """Generate audio array from semantic input.
 
@@ -39,6 +43,7 @@ def semantic_to_waveform(
         semantic_tokens: semantic token output from `text_to_semantic`
         history_prompt: history choice for audio cloning
         temp: generation temperature (1.0 more diverse, 0.0 more conservative)
+        silent: disable progress bar
 
     Returns:
         numpy audio array at sample frequency 24khz
@@ -47,6 +52,7 @@ def semantic_to_waveform(
         semantic_tokens,
         history_prompt=history_prompt,
         temp=temp,
+        silent=silent,
     )
     x_fine_gen = generate_fine(
         x_coarse_gen,
@@ -62,6 +68,7 @@ def generate_audio(
     history_prompt: Optional[str] = None,
     text_temp: float = 0.7,
     waveform_temp: float = 0.7,
+    silent: bool = False,
 ):
     """Generate audio array from input text.
 
@@ -70,10 +77,15 @@ def generate_audio(
         history_prompt: history choice for audio cloning
         text_temp: generation temperature (1.0 more diverse, 0.0 more conservative)
         waveform_temp: generation temperature (1.0 more diverse, 0.0 more conservative)
+        silent: disable progress bar
 
     Returns:
         numpy audio array at sample frequency 24khz
     """
-    x_semantic = text_to_semantic(text, history_prompt=history_prompt, temp=text_temp)
-    audio_arr = semantic_to_waveform(x_semantic, history_prompt=history_prompt, temp=waveform_temp)
+    x_semantic = text_to_semantic(
+        text, history_prompt=history_prompt, temp=text_temp, silent=silent,
+    )
+    audio_arr = semantic_to_waveform(
+        x_semantic, history_prompt=history_prompt, temp=waveform_temp, silent=silent,
+    )
     return audio_arr

bark/generation.py

@@ -137,9 +137,9 @@ def _parse_s3_filepath(s3_filepath):
 def _download(from_s3_path, to_local_path):
     os.makedirs(CACHE_DIR, exist_ok=True)
     response = requests.get(from_s3_path, stream=True)
-    total_size_in_bytes = int(response.headers.get('content-length', 0))
-    block_size = 1024  # 1 Kibibyte
-    progress_bar = tqdm.tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
+    total_size_in_bytes = int(response.headers.get("content-length", 0))
+    block_size = 1024
+    progress_bar = tqdm.tqdm(total=total_size_in_bytes, unit="iB", unit_scale=True)
     with open(to_local_path, "wb") as file:
         for data in response.iter_content(block_size):
             progress_bar.update(len(data))
@@ -191,7 +191,7 @@ def clean_models(model_key=None):
 
 def _load_model(ckpt_path, device, model_type="text"):
     if "cuda" not in device:
-        logger.warning("No GPU being used. Careful, Inference might be extremely slow!")
+        logger.warning("No GPU being used. Careful, inference might be extremely slow!")
     if model_type == "text":
         ConfigClass = GPTConfig
         ModelClass = GPT
@@ -207,10 +207,10 @@ def _load_model(ckpt_path, device, model_type="text"):
         os.path.exists(ckpt_path) and
         _md5(ckpt_path) != REMOTE_MODEL_PATHS[model_type]["checksum"]
     ):
-        logger.warning(f"found outdated {model_type} model, removing...")
+        logger.warning(f"found outdated {model_type} model, removing.")
         os.remove(ckpt_path)
     if not os.path.exists(ckpt_path):
-        logger.info(f"{model_type} model not found, downloading...")
+        logger.info(f"{model_type} model not found, downloading into `{CACHE_DIR}`.")
         _download(REMOTE_MODEL_PATHS[model_type]["path"], ckpt_path)
     checkpoint = torch.load(ckpt_path, map_location=device)
     # this is a hack