small updates

README.md

@@ -72,7 +72,7 @@ audio_array = generate_audio(text_prompt)
 
 ### 🎤 Voice Presets and Voice/Audio Cloning
 
-Bark has the capability to fully clone voices - including tone, pitch, emotion and prosody. The model also attempts to preserve music, ambient noise, etc. from input audio. However, to mitigate misuse of this technology, we limit the audio history prompts to a limited set of Suno-provided, fully synthetic options to choose from for each language. Specify following the pattern: `{lang_code}_speaker_{number}`.
+Bark has the capability to fully clone voices - including tone, pitch, emotion and prosody. The model also attempts to preserve music, ambient noise, etc. from input audio. However, to mitigate misuse of this technology, we limit the audio history prompts to a limited set of Suno-provided, fully synthetic options to choose from for each language. Specify following the pattern: `{lang_code}_speaker_{0-9}`.
 
 ```python
 text_prompt = """

bark/api.py

@@ -9,13 +9,15 @@ def text_to_semantic(
     text: str,
     history_prompt: Optional[str] = None,
     temp: float = 0.7,
+    silent: bool = False,
 ):
     """Generate semantic array from text.
 
     Args:
         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
 
@@ -32,13 +35,15 @@ 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.
 
     Args:
         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

model-card.md

@@ -8,7 +8,7 @@ The following is additional information about the models released here.
 
 Bark is a series of three transformer models that turn text into audio.
 ### Text to semantic tokens
- - Input: text, tokenized with [BERT tokenizer from huggingface](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertTokenizer)
+ - Input: text, tokenized with [BERT tokenizer from Hugging Face](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertTokenizer)
  - Output: semantic tokens that encode the audio to be generated
 
 ### Semantic to coarse tokens