``` ├── .gitignore ├── LICENSE ├── README.md ├── generator.py ├── models.py ├── requirements.txt ├── run_csm.py ├── setup.py ├── watermarking.py ``` ## /.gitignore ```gitignore path="/.gitignore" # Python __pycache__/ *.py[cod] *$py.class *.so .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ *.egg-info/ .installed.cfg *.egg # Virtual Environment .env .venv env/ venv/ ENV/ # IDE .idea/ .vscode/ *.swp *.swo # Project specific .python-version *.wav output_*/ basic_audio.wav full_conversation.wav context_audio.wav # Model files *.pt *.ckpt ``` ## /LICENSE ``` path="/LICENSE" Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. 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We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ``` ## /README.md # CSM **2025/03/13** - We are releasing the 1B CSM variant. The checkpoint is [hosted on Hugging Face](https://huggingface.co/sesame/csm_1b). --- CSM (Conversational Speech Model) is a speech generation model from [Sesame](https://www.sesame.com) that generates RVQ audio codes from text and audio inputs. The model architecture employs a [Llama](https://www.llama.com/) backbone and a smaller audio decoder that produces [Mimi](https://huggingface.co/kyutai/mimi) audio codes. A fine-tuned variant of CSM powers the [interactive voice demo](https://www.sesame.com/voicedemo) shown in our [blog post](https://www.sesame.com/research/crossing_the_uncanny_valley_of_voice). A hosted [Hugging Face space](https://huggingface.co/spaces/sesame/csm-1b) is also available for testing audio generation. ## Requirements * A CUDA-compatible GPU * The code has been tested on CUDA 12.4 and 12.6, but it may also work on other versions * Similarly, Python 3.10 is recommended, but newer versions may be fine * For some audio operations, `ffmpeg` may be required * Access to the following Hugging Face models: * [Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) * [CSM-1B](https://huggingface.co/sesame/csm-1b) ### Setup ```bash git clone git@github.com:SesameAILabs/csm.git cd csm python3.10 -m venv .venv source .venv/bin/activate pip install -r requirements.txt # Disable lazy compilation in Mimi export NO_TORCH_COMPILE=1 # You will need access to CSM-1B and Llama-3.2-1B huggingface-cli login ``` ### Windows Setup The `triton` package cannot be installed in Windows. Instead use `pip install triton-windows`. ## Quickstart This script will generate a conversation between 2 characters, using a prompt for each character. ```bash python run_csm.py ``` ## Usage If you want to write your own applications with CSM, the following examples show basic usage. #### Generate a sentence This will use a random speaker identity, as no prompt or context is provided. ```python from generator import load_csm_1b import torchaudio import torch if torch.backends.mps.is_available(): device = "mps" elif torch.cuda.is_available(): device = "cuda" else: device = "cpu" generator = load_csm_1b(device=device) audio = generator.generate( text="Hello from Sesame.", speaker=0, context=[], max_audio_length_ms=10_000, ) torchaudio.save("audio.wav", audio.unsqueeze(0).cpu(), generator.sample_rate) ``` #### Generate with context CSM sounds best when provided with context. You can prompt or provide context to the model using a `Segment` for each speaker's utterance. NOTE: The following example is instructional and the audio files do not exist. It is intended as an example for using context with CSM. ```python from generator import Segment speakers = [0, 1, 0, 0] transcripts = [ "Hey how are you doing.", "Pretty good, pretty good.", "I'm great.", "So happy to be speaking to you.", ] audio_paths = [ "utterance_0.wav", "utterance_1.wav", "utterance_2.wav", "utterance_3.wav", ] def load_audio(audio_path): audio_tensor, sample_rate = torchaudio.load(audio_path) audio_tensor = torchaudio.functional.resample( audio_tensor.squeeze(0), orig_freq=sample_rate, new_freq=generator.sample_rate ) return audio_tensor segments = [ Segment(text=transcript, speaker=speaker, audio=load_audio(audio_path)) for transcript, speaker, audio_path in zip(transcripts, speakers, audio_paths) ] audio = generator.generate( text="Me too, this is some cool stuff huh?", speaker=1, context=segments, max_audio_length_ms=10_000, ) torchaudio.save("audio.wav", audio.unsqueeze(0).cpu(), generator.sample_rate) ``` ## FAQ **Does this model come with any voices?** The model open-sourced here is a base generation model. It is capable of producing a variety of voices, but it has not been fine-tuned on any specific voice. **Can I converse with the model?** CSM is trained to be an audio generation model and not a general-purpose multimodal LLM. It cannot generate text. We suggest using a separate LLM for text generation. **Does it support other languages?** The model has some capacity for non-English languages due to data contamination in the training data, but it likely won't do well. ## Misuse and abuse ⚠️ This project provides a high-quality speech generation model for research and educational purposes. While we encourage responsible and ethical use, we **explicitly prohibit** the following: - **Impersonation or Fraud**: Do not use this model to generate speech that mimics real individuals without their explicit consent. - **Misinformation or Deception**: Do not use this model to create deceptive or misleading content, such as fake news or fraudulent calls. - **Illegal or Harmful Activities**: Do not use this model for any illegal, harmful, or malicious purposes. By using this model, you agree to comply with all applicable laws and ethical guidelines. We are **not responsible** for any misuse, and we strongly condemn unethical applications of this technology. --- ## Authors Johan Schalkwyk, Ankit Kumar, Dan Lyth, Sefik Emre Eskimez, Zack Hodari, Cinjon Resnick, Ramon Sanabria, Raven Jiang, and the Sesame team. ## /generator.py ```py path="/generator.py" from dataclasses import dataclass from typing import List, Tuple import torch import torchaudio from huggingface_hub import hf_hub_download from models import Model from moshi.models import loaders from tokenizers.processors import TemplateProcessing from transformers import AutoTokenizer from watermarking import CSM_1B_GH_WATERMARK, load_watermarker, watermark @dataclass class Segment: speaker: int text: str # (num_samples,), sample_rate = 24_000 audio: torch.Tensor def load_llama3_tokenizer(): """ https://github.com/huggingface/transformers/issues/22794#issuecomment-2092623992 """ tokenizer_name = "meta-llama/Llama-3.2-1B" tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) bos = tokenizer.bos_token eos = tokenizer.eos_token tokenizer._tokenizer.post_processor = TemplateProcessing( single=f"{bos}:0 $A:0 {eos}:0", pair=f"{bos}:0 $A:0 {eos}:0 {bos}:1 $B:1 {eos}:1", special_tokens=[(f"{bos}", tokenizer.bos_token_id), (f"{eos}", tokenizer.eos_token_id)], ) return tokenizer class Generator: def __init__( self, model: Model, ): self._model = model self._model.setup_caches(1) self._text_tokenizer = load_llama3_tokenizer() device = next(model.parameters()).device mimi_weight = hf_hub_download(loaders.DEFAULT_REPO, loaders.MIMI_NAME) mimi = loaders.get_mimi(mimi_weight, device=device) mimi.set_num_codebooks(32) self._audio_tokenizer = mimi self._watermarker = load_watermarker(device=device) self.sample_rate = mimi.sample_rate self.device = device def _tokenize_text_segment(self, text: str, speaker: int) -> Tuple[torch.Tensor, torch.Tensor]: frame_tokens = [] frame_masks = [] text_tokens = self._text_tokenizer.encode(f"[{speaker}]{text}") text_frame = torch.zeros(len(text_tokens), 33).long() text_frame_mask = torch.zeros(len(text_tokens), 33).bool() text_frame[:, -1] = torch.tensor(text_tokens) text_frame_mask[:, -1] = True frame_tokens.append(text_frame.to(self.device)) frame_masks.append(text_frame_mask.to(self.device)) return torch.cat(frame_tokens, dim=0), torch.cat(frame_masks, dim=0) def _tokenize_audio(self, audio: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: assert audio.ndim == 1, "Audio must be single channel" frame_tokens = [] frame_masks = [] # (K, T) audio = audio.to(self.device) audio_tokens = self._audio_tokenizer.encode(audio.unsqueeze(0).unsqueeze(0))[0] # add EOS frame eos_frame = torch.zeros(audio_tokens.size(0), 1).to(self.device) audio_tokens = torch.cat([audio_tokens, eos_frame], dim=1) audio_frame = torch.zeros(audio_tokens.size(1), 33).long().to(self.device) audio_frame_mask = torch.zeros(audio_tokens.size(1), 33).bool().to(self.device) audio_frame[:, :-1] = audio_tokens.transpose(0, 1) audio_frame_mask[:, :-1] = True frame_tokens.append(audio_frame) frame_masks.append(audio_frame_mask) return torch.cat(frame_tokens, dim=0), torch.cat(frame_masks, dim=0) def _tokenize_segment(self, segment: Segment) -> Tuple[torch.Tensor, torch.Tensor]: """ Returns: (seq_len, 33), (seq_len, 33) """ text_tokens, text_masks = self._tokenize_text_segment(segment.text, segment.speaker) audio_tokens, audio_masks = self._tokenize_audio(segment.audio) return torch.cat([text_tokens, audio_tokens], dim=0), torch.cat([text_masks, audio_masks], dim=0) @torch.inference_mode() def generate( self, text: str, speaker: int, context: List[Segment], max_audio_length_ms: float = 90_000, temperature: float = 0.9, topk: int = 50, ) -> torch.Tensor: self._model.reset_caches() max_generation_len = int(max_audio_length_ms / 80) tokens, tokens_mask = [], [] for segment in context: segment_tokens, segment_tokens_mask = self._tokenize_segment(segment) tokens.append(segment_tokens) tokens_mask.append(segment_tokens_mask) gen_segment_tokens, gen_segment_tokens_mask = self._tokenize_text_segment(text, speaker) tokens.append(gen_segment_tokens) tokens_mask.append(gen_segment_tokens_mask) prompt_tokens = torch.cat(tokens, dim=0).long().to(self.device) prompt_tokens_mask = torch.cat(tokens_mask, dim=0).bool().to(self.device) samples = [] curr_tokens = prompt_tokens.unsqueeze(0) curr_tokens_mask = prompt_tokens_mask.unsqueeze(0) curr_pos = torch.arange(0, prompt_tokens.size(0)).unsqueeze(0).long().to(self.device) max_seq_len = 2048 max_context_len = max_seq_len - max_generation_len if curr_tokens.size(1) >= max_context_len: raise ValueError( f"Inputs too long, must be below max_seq_len - max_generation_len: {max_context_len}" ) for _ in range(max_generation_len): sample = self._model.generate_frame(curr_tokens, curr_tokens_mask, curr_pos, temperature, topk) if torch.all(sample == 0): break # eos samples.append(sample) curr_tokens = torch.cat([sample, torch.zeros(1, 1).long().to(self.device)], dim=1).unsqueeze(1) curr_tokens_mask = torch.cat( [torch.ones_like(sample).bool(), torch.zeros(1, 1).bool().to(self.device)], dim=1 ).unsqueeze(1) curr_pos = curr_pos[:, -1:] + 1 audio = self._audio_tokenizer.decode(torch.stack(samples).permute(1, 2, 0)).squeeze(0).squeeze(0) # This applies an imperceptible watermark to identify audio as AI-generated. # Watermarking ensures transparency, dissuades misuse, and enables traceability. # Please be a responsible AI citizen and keep the watermarking in place. # If using CSM 1B in another application, use your own private key and keep it secret. audio, wm_sample_rate = watermark(self._watermarker, audio, self.sample_rate, CSM_1B_GH_WATERMARK) audio = torchaudio.functional.resample(audio, orig_freq=wm_sample_rate, new_freq=self.sample_rate) return audio def load_csm_1b(device: str = "cuda") -> Generator: model = Model.from_pretrained("sesame/csm-1b") model.to(device=device, dtype=torch.bfloat16) generator = Generator(model) return generator ``` ## /models.py ```py path="/models.py" from dataclasses import dataclass import torch import torch.nn as nn import torchtune from huggingface_hub import PyTorchModelHubMixin from torchtune.models import llama3_2 def llama3_2_1B() -> torchtune.modules.transformer.TransformerDecoder: return llama3_2.llama3_2( vocab_size=128_256, num_layers=16, num_heads=32, num_kv_heads=8, embed_dim=2048, max_seq_len=2048, intermediate_dim=8192, attn_dropout=0.0, norm_eps=1e-5, rope_base=500_000, scale_factor=32, ) def llama3_2_100M() -> torchtune.modules.transformer.TransformerDecoder: return llama3_2.llama3_2( vocab_size=128_256, num_layers=4, num_heads=8, num_kv_heads=2, embed_dim=1024, max_seq_len=2048, intermediate_dim=8192, attn_dropout=0.0, norm_eps=1e-5, rope_base=500_000, scale_factor=32, ) FLAVORS = { "llama-1B": llama3_2_1B, "llama-100M": llama3_2_100M, } def _prepare_transformer(model): embed_dim = model.tok_embeddings.embedding_dim model.tok_embeddings = nn.Identity() model.output = nn.Identity() return model, embed_dim def _create_causal_mask(seq_len: int, device: torch.device): return torch.tril(torch.ones(seq_len, seq_len, dtype=torch.bool, device=device)) def _index_causal_mask(mask: torch.Tensor, input_pos: torch.Tensor): """ Args: mask: (max_seq_len, max_seq_len) input_pos: (batch_size, seq_len) Returns: (batch_size, seq_len, max_seq_len) """ r = mask[input_pos, :] return r def _multinomial_sample_one_no_sync(probs): # Does multinomial sampling without a cuda synchronization q = torch.empty_like(probs).exponential_(1) return torch.argmax(probs / q, dim=-1, keepdim=True).to(dtype=torch.int) def sample_topk(logits: torch.Tensor, topk: int, temperature: float): logits = logits / temperature filter_value: float = -float("Inf") indices_to_remove = logits < torch.topk(logits, topk)[0][..., -1, None] scores_processed = logits.masked_fill(indices_to_remove, filter_value) scores_processed = torch.nn.functional.log_softmax(scores_processed, dim=-1) probs = torch.nn.functional.softmax(scores_processed, dim=-1) sample_token = _multinomial_sample_one_no_sync(probs) return sample_token @dataclass class ModelArgs: backbone_flavor: str decoder_flavor: str text_vocab_size: int audio_vocab_size: int audio_num_codebooks: int class Model( nn.Module, PyTorchModelHubMixin, repo_url="https://github.com/SesameAILabs/csm", pipeline_tag="text-to-speech", license="apache-2.0", ): def __init__(self, config: ModelArgs): super().__init__() self.config = config self.backbone, backbone_dim = _prepare_transformer(FLAVORS[config.backbone_flavor]()) self.decoder, decoder_dim = _prepare_transformer(FLAVORS[config.decoder_flavor]()) self.text_embeddings = nn.Embedding(config.text_vocab_size, backbone_dim) self.audio_embeddings = nn.Embedding(config.audio_vocab_size * config.audio_num_codebooks, backbone_dim) self.projection = nn.Linear(backbone_dim, decoder_dim, bias=False) self.codebook0_head = nn.Linear(backbone_dim, config.audio_vocab_size, bias=False) self.audio_head = nn.Parameter(torch.empty(config.audio_num_codebooks - 1, decoder_dim, config.audio_vocab_size)) def setup_caches(self, max_batch_size: int) -> torch.Tensor: """Setup KV caches and return a causal mask.""" dtype = next(self.parameters()).dtype device = next(self.parameters()).device with device: self.backbone.setup_caches(max_batch_size, dtype) self.decoder.setup_caches(max_batch_size, dtype, decoder_max_seq_len=self.config.audio_num_codebooks) self.register_buffer("backbone_causal_mask", _create_causal_mask(self.backbone.max_seq_len, device)) self.register_buffer("decoder_causal_mask", _create_causal_mask(self.config.audio_num_codebooks, device)) def generate_frame( self, tokens: torch.Tensor, tokens_mask: torch.Tensor, input_pos: torch.Tensor, temperature: float, topk: int, ) -> torch.Tensor: """ Args: tokens: (batch_size, seq_len, audio_num_codebooks+1) tokens_mask: (batch_size, seq_len, audio_num_codebooks+1) input_pos: (batch_size, seq_len) positions for each token mask: (batch_size, seq_len, max_seq_len Returns: (batch_size, audio_num_codebooks) sampled tokens """ dtype = next(self.parameters()).dtype b, s, _ = tokens.size() assert self.backbone.caches_are_enabled(), "backbone caches are not enabled" curr_backbone_mask = _index_causal_mask(self.backbone_causal_mask, input_pos) embeds = self._embed_tokens(tokens) masked_embeds = embeds * tokens_mask.unsqueeze(-1) h = masked_embeds.sum(dim=2) h = self.backbone(h, input_pos=input_pos, mask=curr_backbone_mask).to(dtype=dtype) last_h = h[:, -1, :] c0_logits = self.codebook0_head(last_h) c0_sample = sample_topk(c0_logits, topk, temperature) c0_embed = self._embed_audio(0, c0_sample) curr_h = torch.cat([last_h.unsqueeze(1), c0_embed], dim=1) curr_sample = c0_sample.clone() curr_pos = torch.arange(0, curr_h.size(1), device=curr_h.device).unsqueeze(0).repeat(curr_h.size(0), 1) # Decoder caches must be reset every frame. self.decoder.reset_caches() for i in range(1, self.config.audio_num_codebooks): curr_decoder_mask = _index_causal_mask(self.decoder_causal_mask, curr_pos) decoder_h = self.decoder(self.projection(curr_h), input_pos=curr_pos, mask=curr_decoder_mask).to( dtype=dtype ) ci_logits = torch.mm(decoder_h[:, -1, :], self.audio_head[i - 1]) ci_sample = sample_topk(ci_logits, topk, temperature) ci_embed = self._embed_audio(i, ci_sample) curr_h = ci_embed curr_sample = torch.cat([curr_sample, ci_sample], dim=1) curr_pos = curr_pos[:, -1:] + 1 return curr_sample def reset_caches(self): self.backbone.reset_caches() self.decoder.reset_caches() def _embed_audio(self, codebook: int, tokens: torch.Tensor) -> torch.Tensor: return self.audio_embeddings(tokens + codebook * self.config.audio_vocab_size) def _embed_tokens(self, tokens: torch.Tensor) -> torch.Tensor: text_embeds = self.text_embeddings(tokens[:, :, -1]).unsqueeze(-2) audio_tokens = tokens[:, :, :-1] + ( self.config.audio_vocab_size * torch.arange(self.config.audio_num_codebooks, device=tokens.device) ) audio_embeds = self.audio_embeddings(audio_tokens.view(-1)).reshape( tokens.size(0), tokens.size(1), self.config.audio_num_codebooks, -1 ) return torch.cat([audio_embeds, text_embeds], dim=-2) ``` ## /requirements.txt torch==2.4.0 torchaudio==2.4.0 tokenizers==0.21.0 transformers==4.49.0 huggingface_hub==0.28.1 moshi==0.2.2 torchtune==0.4.0 torchao==0.9.0 silentcipher @ git+https://github.com/SesameAILabs/silentcipher@master ## /run_csm.py ```py path="/run_csm.py" import os import torch import torchaudio from huggingface_hub import hf_hub_download from generator import load_csm_1b, Segment from dataclasses import dataclass # Disable Triton compilation os.environ["NO_TORCH_COMPILE"] = "1" # Default prompts are available at https://hf.co/sesame/csm-1b prompt_filepath_conversational_a = hf_hub_download( repo_id="sesame/csm-1b", filename="prompts/conversational_a.wav" ) prompt_filepath_conversational_b = hf_hub_download( repo_id="sesame/csm-1b", filename="prompts/conversational_b.wav" ) SPEAKER_PROMPTS = { "conversational_a": { "text": ( "like revising for an exam I'd have to try and like keep up the momentum because I'd " "start really early I'd be like okay I'm gonna start revising now and then like " "you're revising for ages and then I just like start losing steam I didn't do that " "for the exam we had recently to be fair that was a more of a last minute scenario " "but like yeah I'm trying to like yeah I noticed this yesterday that like Mondays I " "sort of start the day with this not like a panic but like a" ), "audio": prompt_filepath_conversational_a }, "conversational_b": { "text": ( "like a super Mario level. Like it's very like high detail. And like, once you get " "into the park, it just like, everything looks like a computer game and they have all " "these, like, you know, if, if there's like a, you know, like in a Mario game, they " "will have like a question block. And if you like, you know, punch it, a coin will " "come out. So like everyone, when they come into the park, they get like this little " "bracelet and then you can go punching question blocks around." ), "audio": prompt_filepath_conversational_b } } def load_prompt_audio(audio_path: str, target_sample_rate: int) -> torch.Tensor: audio_tensor, sample_rate = torchaudio.load(audio_path) audio_tensor = audio_tensor.squeeze(0) # Resample is lazy so we can always call it audio_tensor = torchaudio.functional.resample( audio_tensor, orig_freq=sample_rate, new_freq=target_sample_rate ) return audio_tensor def prepare_prompt(text: str, speaker: int, audio_path: str, sample_rate: int) -> Segment: audio_tensor = load_prompt_audio(audio_path, sample_rate) return Segment(text=text, speaker=speaker, audio=audio_tensor) def main(): # Select the best available device, skipping MPS due to float64 limitations if torch.cuda.is_available(): device = "cuda" else: device = "cpu" print(f"Using device: {device}") # Load model generator = load_csm_1b(device) # Prepare prompts prompt_a = prepare_prompt( SPEAKER_PROMPTS["conversational_a"]["text"], 0, SPEAKER_PROMPTS["conversational_a"]["audio"], generator.sample_rate ) prompt_b = prepare_prompt( SPEAKER_PROMPTS["conversational_b"]["text"], 1, SPEAKER_PROMPTS["conversational_b"]["audio"], generator.sample_rate ) # Generate conversation conversation = [ {"text": "Hey how are you doing?", "speaker_id": 0}, {"text": "Pretty good, pretty good. How about you?", "speaker_id": 1}, {"text": "I'm great! So happy to be speaking with you today.", "speaker_id": 0}, {"text": "Me too! This is some cool stuff, isn't it?", "speaker_id": 1} ] # Generate each utterance generated_segments = [] prompt_segments = [prompt_a, prompt_b] for utterance in conversation: print(f"Generating: {utterance['text']}") audio_tensor = generator.generate( text=utterance['text'], speaker=utterance['speaker_id'], context=prompt_segments + generated_segments, max_audio_length_ms=10_000, ) generated_segments.append(Segment(text=utterance['text'], speaker=utterance['speaker_id'], audio=audio_tensor)) # Concatenate all generations all_audio = torch.cat([seg.audio for seg in generated_segments], dim=0) torchaudio.save( "full_conversation.wav", all_audio.unsqueeze(0).cpu(), generator.sample_rate ) print("Successfully generated full_conversation.wav") if __name__ == "__main__": main() ``` ## /setup.py ```py path="/setup.py" from setuptools import setup, find_packages import os # Read requirements from requirements.txt with open('requirements.txt') as f: requirements = [line.strip() for line in f if line.strip() and not line.startswith('#')] setup( name='csm', version='0.1.0', packages=find_packages(), install_requires=requirements, ) ``` ## /watermarking.py ```py path="/watermarking.py" import argparse import silentcipher import torch import torchaudio # This watermark key is public, it is not secure. # If using CSM 1B in another application, use a new private key and keep it secret. CSM_1B_GH_WATERMARK = [212, 211, 146, 56, 201] def cli_check_audio() -> None: parser = argparse.ArgumentParser() parser.add_argument("--audio_path", type=str, required=True) args = parser.parse_args() check_audio_from_file(args.audio_path) def load_watermarker(device: str = "cuda") -> silentcipher.server.Model: model = silentcipher.get_model( model_type="44.1k", device=device, ) return model @torch.inference_mode() def watermark( watermarker: silentcipher.server.Model, audio_array: torch.Tensor, sample_rate: int, watermark_key: list[int], ) -> tuple[torch.Tensor, int]: audio_array_44khz = torchaudio.functional.resample(audio_array, orig_freq=sample_rate, new_freq=44100) encoded, _ = watermarker.encode_wav(audio_array_44khz, 44100, watermark_key, calc_sdr=False, message_sdr=36) output_sample_rate = min(44100, sample_rate) encoded = torchaudio.functional.resample(encoded, orig_freq=44100, new_freq=output_sample_rate) return encoded, output_sample_rate @torch.inference_mode() def verify( watermarker: silentcipher.server.Model, watermarked_audio: torch.Tensor, sample_rate: int, watermark_key: list[int], ) -> bool: watermarked_audio_44khz = torchaudio.functional.resample(watermarked_audio, orig_freq=sample_rate, new_freq=44100) result = watermarker.decode_wav(watermarked_audio_44khz, 44100, phase_shift_decoding=True) is_watermarked = result["status"] if is_watermarked: is_csm_watermarked = result["messages"][0] == watermark_key else: is_csm_watermarked = False return is_watermarked and is_csm_watermarked def check_audio_from_file(audio_path: str) -> None: watermarker = load_watermarker(device="cuda") audio_array, sample_rate = load_audio(audio_path) is_watermarked = verify(watermarker, audio_array, sample_rate, CSM_1B_GH_WATERMARK) outcome = "Watermarked" if is_watermarked else "Not watermarked" print(f"{outcome}: {audio_path}") def load_audio(audio_path: str) -> tuple[torch.Tensor, int]: audio_array, sample_rate = torchaudio.load(audio_path) audio_array = audio_array.mean(dim=0) return audio_array, int(sample_rate) if __name__ == "__main__": cli_check_audio() ``` The better and more specific the context, the better the LLM can follow instructions. If the context seems verbose, the user can refine the filter using uithub. Thank you for using https://uithub.com - Perfect LLM context for any GitHub repo.