import TTS

README.md

@@ -16,13 +16,13 @@ language:
 thumbnail: >-
   https://user-images.githubusercontent.com/5068315/230698495-cbb1ced9-c911-4c9a-941d-a1a4a1286ac6.png
 library: bark
-license: mit
+license: cc-by-nc-4.0
 tags:
 - bark
 - audio
 - text-to-speech
 pipeline_tag: text-to-speech
-inference: true
+inference: false
 ---
 
 # Bark
@@ -69,35 +69,23 @@ Try out Bark yourself!
 
 ## 🤗 Transformers Usage
 
+
 You can run Bark locally with the 🤗 Transformers library from version 4.31.0 onwards.
 
-1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers) and scipy:
+1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers) from main:
 
 ```
-pip install --upgrade pip
-pip install --upgrade transformers scipy
-```
-
-2. Run inference via the `Text-to-Speech` (TTS) pipeline. You can infer the bark model via the TTS pipeline in just a few lines of code!
-
-```python
-from transformers import pipeline
-import scipy
-
-synthesiser = pipeline("text-to-speech", "suno/bark")
-
-speech = synthesiser("Hello, my dog is cooler than you!", forward_params={"do_sample": True})
-
-scipy.io.wavfile.write("bark_out.wav", rate=speech["sampling_rate"], data=speech["audio"])
+pip install git+https://github.com/huggingface/transformers.git
 ```
 
-3. Run inference via the Transformers modelling code. You can use the processor + generate code to convert text into a mono 24 kHz speech waveform for more fine-grained control.
+2. Run the following Python code to generate speech samples:
 
 ```python
 from transformers import AutoProcessor, AutoModel
 
-processor = AutoProcessor.from_pretrained("suno/bark")
-model = AutoModel.from_pretrained("suno/bark")
+
+processor = AutoProcessor.from_pretrained("suno/bark-small")
+model = AutoModel.from_pretrained("suno/bark-small")
 
 inputs = processor(
     text=["Hello, my name is Suno. And, uh — and I like pizza. [laughs] But I also have other interests such as playing tic tac toe."],
@@ -107,7 +95,7 @@ inputs = processor(
 speech_values = model.generate(**inputs, do_sample=True)
 ```
 
-4. Listen to the speech samples either in an ipynb notebook:
+3. Listen to the speech samples either in an ipynb notebook:
 
 ```python
 from IPython.display import Audio
@@ -133,7 +121,7 @@ You can also run Bark locally through the original [Bark library]((https://githu
 
 1. First install the [`bark` library](https://github.com/suno-ai/bark)
 
-2. Run the following Python code:
+3. Run the following Python code:
 
 ```python
 from bark import SAMPLE_RATE, generate_audio, preload_models

config.json

@@ -80,7 +80,7 @@
     "use_cache": true
   },
   "codec_config": {
-    "_name_or_path": "facebook/encodec_24khz",
+    "_name_or_path": "ArthurZ/encodec_24khz",
     "add_cross_attention": false,
     "architectures": [
       "EncodecModel"