from mlx_lm.sample_utils import make_sampler from huggingface_hub import hf_hub_download from csm_mlx import CSM, csm_1b, generate import audiofile import numpy as np # Initialize the model csm = CSM(csm_1b()) # csm_1b() is a configuration for the CSM model. weight = hf_hub_download(repo_id="senstella/csm-1b-mlx", filename="ckpt.safetensors") csm.load_weights(weight) # Generate audio from text audio = generate( csm, text="Hello from Sesame.", speaker=0, context=[], max_audio_length_ms=10_000, sampler=make_sampler(temp=0.8, top_k=50), # Put mlx_lm's sampler here! Supports: temp, top_p, min_p, min_tokens_to_keep, top_k. # Additionally, you can provide `stream` argument to specify what device to use for generation. # https://ml-explore.github.io/mlx/build/html/usage/using_streams.html ) audiofile.write("./audio.wav", np.asarray(audio), 24000)

from csm_mlx import CSM, csm_1b, generate, Segment import mlx.core as mx # Initialize the model csm = CSM(csm_1b()) weight = hf_hub_download(repo_id="senstella/csm-1b-mlx", filename="ckpt.safetensors") csm.load_weights(weight) # Create previous conversation segments context = [ Segment( speaker=0, text="How are you doing today?", audio=mx.array(...) # Previous audio for this segment ), Segment( speaker=1, text="I'm doing great, thank you!", audio_path="~/somewhere_in_the_universe/stuff.wav" # Or you can specify the audio path too! ) ] # Generate a response in the conversation audio = generate( csm, text="That's wonderful to hear!", speaker=0, context=context, max_audio_length_ms=5_000 # If you don't provide any sampler, greedy sampling will be used. )

from mlx import nn from mlx_lm.sample_utils import make_sampler, make_logits_processors from huggingface_hub import hf_hub_download from csm_mlx import CSM, csm_1b, generate import audiofile import numpy as np # Initialize the model csm = CSM(csm_1b()) # csm_1b() is a configuration for the CSM model. weight = hf_hub_download(repo_id="senstella/csm-1b-mlx", filename="ckpt.safetensors") csm.load_weights(weight) # Quantize # If you want, specify the `group_size`, `bits`. # https://ml-explore.github.io/mlx/build/html/python/_autosummary/mlx.core.quantize.html nn.quantize(csm) # Generate audio from text audio = generate( csm, text="Hello from Sesame.", speaker=0, context=[], max_audio_length_ms=10_000, sampler=make_sampler(temp=0.8, top_k=50), # Put mlx_lm's sampler here! Supports: temp, top_p, min_p, min_tokens_to_keep, top_k. # If the model doesn't end the sequence properly, you can try to adjust the sampler parameters # or set eos_token logit bias via logits_processors=make_logits_processors(logit_bias={0: 4}) # Additionally, you can provide `stream` argument to specify what device to use for generation. # https://ml-explore.github.io/mlx/build/html/usage/using_streams.html ) audiofile.write("./audio.wav", np.asarray(audio), 24000)

from mlx import nn from mlx_lm.sample_utils import make_sampler from csm_mlx import CSM, csm_1b from csm_mlx.generation import stream_generate # Initialize model csm = CSM(csm_1b()) csm.load_weights("ckpt.safetensors", strict=True) # nn.quantize(csm) — Speed up nearly real-time on M2 Air, but loses quality. # Stream generate audio chunks for chunk in stream_generate( csm, text="This is an example of streaming audio generation.", speaker=0, context=[], max_audio_length_ms=5000, accumulation_size=1, # Accumulate codebooks and decode at once (increasing = better speed/stability, more initial delay) sampler=make_sampler(temp=0.8, top_k=50), ): # Process each chunk as it's generated

import mlx.core as mx import audiofile import audresample def read_audio(audio_path, sampling_rate=24000) -> mx.array: signal, original_sampling_rate = audiofile.read(audio_path, always_2d=True) signal = audresample.resample(signal, original_sampling_rate, sampling_rate) signal = mx.array(signal) if signal.shape[0] >= 1: signal = signal.mean(axis=0) else: signal = signal.squeeze(0) return signal # (audio_length, )