Update README.md

README.md

@@ -9,13 +9,13 @@ language:
 pipeline_tag: text-to-speech
 ---
 
-# Parler-TTS v0.1
+# Parler-TTS Mini v0.1
 
 <a target="_blank" href="https://huggingface.co/spaces/parler-tts/parler_tts_mini">
   <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/>
 </a>
 
-**Parler-TTS v0.1** is a lightweight text-to-speech (TTS) model, trained on 10.5K hours of audio data, that can generate high-quality, natural sounding speech with features that can be controlled using a simple text prompt (e.g. gender, background noise, speaking rate, pitch and reverberation).
+**Parler-TTS Mini v0.1** is a lightweight text-to-speech (TTS) model, trained on 10.5K hours of audio data, that can generate high-quality, natural sounding speech with features that can be controlled using a simple text prompt (e.g. gender, background noise, speaking rate, pitch and reverberation).
 It is the first release model from the [Parler-TTS](https://github.com/huggingface/parler-tts) project, which aims to provide the community with TTS training resources and dataset pre-processing code.
 
 ## Usage
@@ -36,8 +36,8 @@ import soundfile as sf
 
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 
-model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler_tts_300M_v0.1").to(device)
-tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler_tts_300M_v0.1")
+model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler_tts_mini_v0.1").to(device)
+tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler_tts_mini_v0.1")
 
 prompt = "Hey, how are you doing today?"
 description = "A female speaker with a slightly low-pitched voice delivers her words quite expressively, in a very confined sounding environment with clear audio quality. She speaks very fast."