Update README.md

README.md

@@ -52,10 +52,10 @@ It models audio as tokens and can generate high-quality audio with consistent st
 ### Key features
 
 1. Extremely small, based on GPT-2 small architecture. The methodology can be extended to any autoregressive transformer-based architecture.
-2. Ultra-fast. Using our [self hosted service option](#self-hosted-service), the model can achieve speeds up to 400 toks/s (4s of audio generation per s) and under 20ms time to first token on RTX6000Ada NVIDIA GPU.
-  1. On RTX6000Ada, it can support a batch size of 1k with full context length of 1024 tokens
-3. Supports voice cloning with small prompts (<5s).
-4. Code mixing text input in 2 languages - English and Hindi.
+2. Ultra-fast. Using our [self hosted service option](#self-hosted-service), on RTX6000Ada NVIDIA GPU the model can achieve speeds up to 400 toks/s (4s of audio generation per s) and under 20ms time to first token.
+3. On RTX6000Ada, it can support a batch size of 1k with full context length of 1024 tokens
+4. Supports voice cloning with small prompts (<5s).
+5. Code mixing text input in 2 languages - English and Hindi.
 
 ### Details
 
@@ -94,11 +94,30 @@ pipe = pipeline(
     trust_remote_code=True
 )
 
-output = pipe(['Hi, my name is Indri and I like to talk.'])
+output = pipe(['Hi, my name is Indri and I like to talk.'], speaker = '[spkr_63]')
 
 torchaudio.save('output.wav', output[0]['audio'][0], sample_rate=24000)
 ```
 
+**Available speakers**
+
+|Speaker ID|Speaker name|
+|---|---|
+|`[spkr_63]`|🇬🇧 👨 book reader|
+|`[spkr_67]`|🇺🇸 👨 influencer|
+|`[spkr_68]`|🇮🇳 👨 book reader|
+|`[spkr_69]`|🇮🇳 👨 book reader|
+|`[spkr_70]`|🇮🇳 👨 motivational speaker|
+|`[spkr_62]`|🇮🇳 👨 book reader heavy|
+|`[spkr_53]`|🇮🇳 👩 recipe reciter|
+|`[spkr_60]`|🇮🇳 👩 book reader|
+|`[spkr_74]`|🇺🇸 👨 book reader|
+|`[spkr_75]`|🇮🇳 👨 entrepreneur|
+|`[spkr_76]`|🇬🇧 👨 nature lover|
+|`[spkr_77]`|🇮🇳 👨 influencer|
+|`[spkr_66]`|🇮🇳 👨 politician|
+
+
 ### Self hosted service
 
 ```bash