diff --git a/README.md b/README.md
index 4f386ecdc2..594777c116 100644
--- a/README.md
+++ b/README.md
@@ -198,17 +198,18 @@ from TTS.api import TTS
 # Get device
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# List available 🐸TTS models and choose the first one
-model_name = TTS().list_models()[0]
+# List available 🐸TTS models
+print(TTS().list_models())
+
 # Init TTS
-tts = TTS(model_name).to(device)
+tts = TTS("tts_models/multilingual/multi-dataset/xtts_v1").to(device)
 
 # Run TTS
-# ❗ Since this model is multi-speaker and multi-lingual, we must set the target speaker and the language
-# Text to speech with a numpy output
-wav = tts.tts("This is a test! This is also a test!!", speaker=tts.speakers[0], language=tts.languages[0])
+# ❗ Since this model is multi-lingual voice cloning model, we must set the target speaker_wav and language
+# Text to speech list of amplitude values as output
+wav = tts.tts(text="Hello world!", speaker_wav="my/cloning/audio.wav", language="en")
 # Text to speech to a file
-tts.tts_to_file(text="Hello world!", speaker=tts.speakers[0], language=tts.languages[0], file_path="output.wav")
+tts.tts_to_file(text="Hello world!", speaker_wav="my/cloning/audio.wav", language="en", file_path="output.wav")
 ```
 
 #### Running a single speaker model
diff --git a/docs/source/inference.md b/docs/source/inference.md
index 3071be4f4d..4de9ecdd14 100644
--- a/docs/source/inference.md
+++ b/docs/source/inference.md
@@ -114,18 +114,24 @@ tts-server --model_name "<type>/<language>/<dataset>/<model_name>" \
 You can run a multi-speaker and multi-lingual model in Python as
 
 ```python
+import torch
 from TTS.api import TTS
 
-# List available 🐸TTS models and choose the first one
-model_name = TTS().list_models()[0]
+# Get device
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# List available 🐸TTS models
+print(TTS().list_models())
+
 # Init TTS
-tts = TTS(model_name)
+tts = TTS("tts_models/multilingual/multi-dataset/xtts_v1").to(device)
+
 # Run TTS
-# ❗ Since this model is multi-speaker and multi-lingual, we must set the target speaker and the language
-# Text to speech with a numpy output
-wav = tts.tts("This is a test! This is also a test!!", speaker=tts.speakers[0], language=tts.languages[0])
+# ❗ Since this model is multi-lingual voice cloning model, we must set the target speaker_wav and language
+# Text to speech list of amplitude values as output
+wav = tts.tts(text="Hello world!", speaker_wav="my/cloning/audio.wav", language="en")
 # Text to speech to a file
-tts.tts_to_file(text="Hello world!", speaker=tts.speakers[0], language=tts.languages[0], file_path="output.wav")
+tts.tts_to_file(text="Hello world!", speaker_wav="my/cloning/audio.wav", language="en", file_path="output.wav")
 ```
 
 #### Here is an example for a single speaker model.