Upload folder using huggingface_hub
Browse files- .gitattributes +4 -0
- Dockerfile +47 -0
- README.md +13 -3
- __pycache__/server.cpython-310.pyc +0 -0
- assistant_female_voice.wav +3 -0
- hotkey.txt +1 -0
- models/dsp/config.json +31 -0
- models/dsp/weights_24khz_1.5kbps_v1.0.pth +3 -0
- models/dsp/weights_24khz_3kbps_v1.0.pth +3 -0
- models/lm/config.json +38 -0
- models/lm/generation_config.json +11 -0
- models/lm/model.safetensors +3 -0
- models/lm/special_tokens_map.json +23 -0
- models/lm/tokenizer.json +3 -0
- models/lm/tokenizer_config.json +2066 -0
- models/v10/config.json +36 -0
- models/v10/generation_config.json +13 -0
- models/v10/model.safetensors +3 -0
- models/v10/special_tokens_map.json +0 -0
- models/v10/tokenizer.json +3 -0
- models/v10/tokenizer_config.json +0 -0
- models/wpt/wpt.pt +3 -0
- requirements.txt +13 -0
- server.py +224 -0
- spk_001.wav +3 -0
- test.ipynb +190 -0
- test_asr.py +23 -0
.gitattributes
CHANGED
|
@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
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| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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| 36 |
+
models/lm/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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| 37 |
+
models/v10/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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| 38 |
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spk_001.wav filter=lfs diff=lfs merge=lfs -text
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assistant_female_voice.wav filter=lfs diff=lfs merge=lfs -text
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Dockerfile
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| 1 |
+
FROM nvidia/cuda:12.3.2-cudnn9-devel-ubuntu22.04
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| 2 |
+
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| 3 |
+
# Set environment variables
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| 4 |
+
ENV PYTHONUNBUFFERED=1 \
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| 5 |
+
DEBIAN_FRONTEND=noninteractive \
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| 6 |
+
CUDA_HOME=/usr/local/cuda \
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| 7 |
+
PATH=/usr/local/cuda/bin:$PATH \
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| 8 |
+
LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH \
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| 9 |
+
NVIDIA_VISIBLE_DEVICES=all \
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| 10 |
+
NVIDIA_DRIVER_CAPABILITIES=compute,utility \
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| 11 |
+
HF_HOME=/app/models \
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| 12 |
+
TRITON_CACHE_DIR=/tmp/triton_cache \
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| 13 |
+
XDG_CACHE_HOME=/tmp \
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| 14 |
+
NUMBA_CACHE_DIR=/tmp/numba_cache
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| 15 |
+
|
| 16 |
+
# Install system dependencies
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| 17 |
+
RUN apt-get update && apt-get install -y --no-install-recommends \
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| 18 |
+
python3 \
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| 19 |
+
python3-pip \
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| 20 |
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python3-dev \
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| 21 |
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build-essential \
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| 22 |
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git \
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| 23 |
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ffmpeg \
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| 24 |
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libsndfile1 \
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| 25 |
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curl \
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| 26 |
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&& rm -rf /var/lib/apt/lists/*
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| 27 |
+
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| 28 |
+
# Upgrade pip and install build tools
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| 29 |
+
RUN python3 -m pip install --upgrade pip setuptools wheel uv
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| 30 |
+
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| 31 |
+
WORKDIR /app
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| 32 |
+
|
| 33 |
+
# Create Numba cache directory
|
| 34 |
+
RUN mkdir -p /tmp/numba_cache /tmp/triton_cache && \
|
| 35 |
+
chown nobody:nogroup /tmp/numba_cache /tmp/triton_cache && \
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| 36 |
+
chmod 700 /tmp/numba_cache /tmp/triton_cache
|
| 37 |
+
|
| 38 |
+
COPY requirements.txt .
|
| 39 |
+
|
| 40 |
+
# Install other requirements
|
| 41 |
+
RUN python3 -m uv pip install --no-cache-dir -r requirements.txt --prerelease=allow
|
| 42 |
+
|
| 43 |
+
COPY . .
|
| 44 |
+
|
| 45 |
+
EXPOSE 8000
|
| 46 |
+
|
| 47 |
+
CMD ["python3", "server.py"]
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README.md
CHANGED
|
@@ -1,3 +1,13 @@
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|
| 1 |
-
---
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| 2 |
-
license: mit
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| 3 |
-
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| 1 |
+
---
|
| 2 |
+
license: mit
|
| 3 |
+
tags:
|
| 4 |
+
- any-to-any
|
| 5 |
+
- omega
|
| 6 |
+
- omegalabs
|
| 7 |
+
- bittensor
|
| 8 |
+
- agi
|
| 9 |
+
---
|
| 10 |
+
|
| 11 |
+
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
|
| 12 |
+
|
| 13 |
+
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
__pycache__/server.cpython-310.pyc
ADDED
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Binary file (6.24 kB). View file
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assistant_female_voice.wav
ADDED
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@@ -0,0 +1,3 @@
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| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:1d712ba6de1d15d52eda96bdc043ce43eb5af4b4ac441b78b6fb0fdaf6683c7a
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| 3 |
+
size 235244
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hotkey.txt
ADDED
|
@@ -0,0 +1 @@
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| 1 |
+
5FTXYCvdLXS4tD6i87PD2dVFXSUJw4BdGomoecSfATr8hNCL
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models/dsp/config.json
ADDED
|
@@ -0,0 +1,31 @@
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| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"DacModel"
|
| 4 |
+
],
|
| 5 |
+
"codebook_dim": 8,
|
| 6 |
+
"codebook_loss_weight": 1.0,
|
| 7 |
+
"codebook_size": 1024,
|
| 8 |
+
"commitment_loss_weight": 0.25,
|
| 9 |
+
"decoder_hidden_size": 1536,
|
| 10 |
+
"downsampling_ratios": [
|
| 11 |
+
2,
|
| 12 |
+
4,
|
| 13 |
+
5,
|
| 14 |
+
8
|
| 15 |
+
],
|
| 16 |
+
"encoder_hidden_size": 64,
|
| 17 |
+
"hidden_size": 1024,
|
| 18 |
+
"hop_length": 512,
|
| 19 |
+
"model_type": "dac",
|
| 20 |
+
"n_codebooks": 4,
|
| 21 |
+
"quantizer_dropout": 0.0,
|
| 22 |
+
"sampling_rate": 24000,
|
| 23 |
+
"torch_dtype": "float32",
|
| 24 |
+
"transformers_version": "4.42.0.dev0",
|
| 25 |
+
"upsampling_ratios": [
|
| 26 |
+
8,
|
| 27 |
+
5,
|
| 28 |
+
4,
|
| 29 |
+
2
|
| 30 |
+
]
|
| 31 |
+
}
|
models/dsp/weights_24khz_1.5kbps_v1.0.pth
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d77ca0b04df942ec64e6a7a162bcac093b1127700acdaec0079f40d32c4405fb
|
| 3 |
+
size 295731578
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models/dsp/weights_24khz_3kbps_v1.0.pth
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:566a68a00dd5f5780aff5bbd80db4fd1d9aa25c639c516e596d3841710b6ffe7
|
| 3 |
+
size 295949662
|
models/lm/config.json
ADDED
|
@@ -0,0 +1,38 @@
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|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "lm",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"LlamaForCausalLM"
|
| 5 |
+
],
|
| 6 |
+
"attention_bias": false,
|
| 7 |
+
"attention_dropout": 0.0,
|
| 8 |
+
"bos_token_id": 128000,
|
| 9 |
+
"eos_token_id": 128001,
|
| 10 |
+
"head_dim": 64,
|
| 11 |
+
"hidden_act": "silu",
|
| 12 |
+
"hidden_size": 2048,
|
| 13 |
+
"initializer_range": 0.02,
|
| 14 |
+
"intermediate_size": 8192,
|
| 15 |
+
"max_position_embeddings": 131072,
|
| 16 |
+
"mlp_bias": false,
|
| 17 |
+
"model_type": "llama",
|
| 18 |
+
"num_attention_heads": 32,
|
| 19 |
+
"num_hidden_layers": 16,
|
| 20 |
+
"num_key_value_heads": 8,
|
| 21 |
+
"pad_token_id": 128004,
|
| 22 |
+
"pretraining_tp": 1,
|
| 23 |
+
"rms_norm_eps": 1e-05,
|
| 24 |
+
"rope_scaling": {
|
| 25 |
+
"factor": 32.0,
|
| 26 |
+
"high_freq_factor": 4.0,
|
| 27 |
+
"low_freq_factor": 1.0,
|
| 28 |
+
"original_max_position_embeddings": 8192,
|
| 29 |
+
"rope_type": "llama3"
|
| 30 |
+
},
|
| 31 |
+
"rope_theta": 500000.0,
|
| 32 |
+
"tie_word_embeddings": true,
|
| 33 |
+
"torch_dtype": "bfloat16",
|
| 34 |
+
"transformers_version": "4.48.0",
|
| 35 |
+
"unsloth_fixed": true,
|
| 36 |
+
"use_cache": false,
|
| 37 |
+
"vocab_size": 128256
|
| 38 |
+
}
|
models/lm/generation_config.json
ADDED
|
@@ -0,0 +1,11 @@
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| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"bos_token_id": 128000,
|
| 4 |
+
"do_sample": true,
|
| 5 |
+
"eos_token_id": 128001,
|
| 6 |
+
"max_length": 131072,
|
| 7 |
+
"pad_token_id": 128004,
|
| 8 |
+
"temperature": 0.6,
|
| 9 |
+
"top_p": 0.9,
|
| 10 |
+
"transformers_version": "4.48.0"
|
| 11 |
+
}
|
models/lm/model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:351bae0df5e4b4c1a3a450f37f43dca51b2dcdaae77d9be279b91bb51f0d899e
|
| 3 |
+
size 2471645576
|
models/lm/special_tokens_map.json
ADDED
|
@@ -0,0 +1,23 @@
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| 1 |
+
{
|
| 2 |
+
"bos_token": {
|
| 3 |
+
"content": "<|begin_of_text|>",
|
| 4 |
+
"lstrip": false,
|
| 5 |
+
"normalized": false,
|
| 6 |
+
"rstrip": false,
|
| 7 |
+
"single_word": false
|
| 8 |
+
},
|
| 9 |
+
"eos_token": {
|
| 10 |
+
"content": "<|end_of_text|>",
|
| 11 |
+
"lstrip": false,
|
| 12 |
+
"normalized": false,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false
|
| 15 |
+
},
|
| 16 |
+
"pad_token": {
|
| 17 |
+
"content": "<|finetune_right_pad_id|>",
|
| 18 |
+
"lstrip": false,
|
| 19 |
+
"normalized": false,
|
| 20 |
+
"rstrip": false,
|
| 21 |
+
"single_word": false
|
| 22 |
+
}
|
| 23 |
+
}
|
models/lm/tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
|
| 3 |
+
size 17209920
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models/lm/tokenizer_config.json
ADDED
|
@@ -0,0 +1,2066 @@
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|
| 1 |
+
{
|
| 2 |
+
"add_bos_token": true,
|
| 3 |
+
"added_tokens_decoder": {
|
| 4 |
+
"128000": {
|
| 5 |
+
"content": "<|begin_of_text|>",
|
| 6 |
+
"lstrip": false,
|
| 7 |
+
"normalized": false,
|
| 8 |
+
"rstrip": false,
|
| 9 |
+
"single_word": false,
|
| 10 |
+
"special": true
|
| 11 |
+
},
|
| 12 |
+
"128001": {
|
| 13 |
+
"content": "<|end_of_text|>",
|
| 14 |
+
"lstrip": false,
|
| 15 |
+
"normalized": false,
|
| 16 |
+
"rstrip": false,
|
| 17 |
+
"single_word": false,
|
| 18 |
+
"special": true
|
| 19 |
+
},
|
| 20 |
+
"128002": {
|
| 21 |
+
"content": "<|reserved_special_token_0|>",
|
| 22 |
+
"lstrip": false,
|
| 23 |
+
"normalized": false,
|
| 24 |
+
"rstrip": false,
|
| 25 |
+
"single_word": false,
|
| 26 |
+
"special": true
|
| 27 |
+
},
|
| 28 |
+
"128003": {
|
| 29 |
+
"content": "<|reserved_special_token_1|>",
|
| 30 |
+
"lstrip": false,
|
| 31 |
+
"normalized": false,
|
| 32 |
+
"rstrip": false,
|
| 33 |
+
"single_word": false,
|
| 34 |
+
"special": true
|
| 35 |
+
},
|
| 36 |
+
"128004": {
|
| 37 |
+
"content": "<|finetune_right_pad_id|>",
|
| 38 |
+
"lstrip": false,
|
| 39 |
+
"normalized": false,
|
| 40 |
+
"rstrip": false,
|
| 41 |
+
"single_word": false,
|
| 42 |
+
"special": true
|
| 43 |
+
},
|
| 44 |
+
"128005": {
|
| 45 |
+
"content": "<|reserved_special_token_2|>",
|
| 46 |
+
"lstrip": false,
|
| 47 |
+
"normalized": false,
|
| 48 |
+
"rstrip": false,
|
| 49 |
+
"single_word": false,
|
| 50 |
+
"special": true
|
| 51 |
+
},
|
| 52 |
+
"128006": {
|
| 53 |
+
"content": "<|start_header_id|>",
|
| 54 |
+
"lstrip": false,
|
| 55 |
+
"normalized": false,
|
| 56 |
+
"rstrip": false,
|
| 57 |
+
"single_word": false,
|
| 58 |
+
"special": true
|
| 59 |
+
},
|
| 60 |
+
"128007": {
|
| 61 |
+
"content": "<|end_header_id|>",
|
| 62 |
+
"lstrip": false,
|
| 63 |
+
"normalized": false,
|
| 64 |
+
"rstrip": false,
|
| 65 |
+
"single_word": false,
|
| 66 |
+
"special": true
|
| 67 |
+
},
|
| 68 |
+
"128008": {
|
| 69 |
+
"content": "<|eom_id|>",
|
| 70 |
+
"lstrip": false,
|
| 71 |
+
"normalized": false,
|
| 72 |
+
"rstrip": false,
|
| 73 |
+
"single_word": false,
|
| 74 |
+
"special": true
|
| 75 |
+
},
|
| 76 |
+
"128009": {
|
| 77 |
+
"content": "<|eot_id|>",
|
| 78 |
+
"lstrip": false,
|
| 79 |
+
"normalized": false,
|
| 80 |
+
"rstrip": false,
|
| 81 |
+
"single_word": false,
|
| 82 |
+
"special": true
|
| 83 |
+
},
|
| 84 |
+
"128010": {
|
| 85 |
+
"content": "<|python_tag|>",
|
| 86 |
+
"lstrip": false,
|
| 87 |
+
"normalized": false,
|
| 88 |
+
"rstrip": false,
|
| 89 |
+
"single_word": false,
|
| 90 |
+
"special": true
|
| 91 |
+
},
|
| 92 |
+
"128011": {
|
| 93 |
+
"content": "<|reserved_special_token_3|>",
|
| 94 |
+
"lstrip": false,
|
| 95 |
+
"normalized": false,
|
| 96 |
+
"rstrip": false,
|
| 97 |
+
"single_word": false,
|
| 98 |
+
"special": true
|
| 99 |
+
},
|
| 100 |
+
"128012": {
|
| 101 |
+
"content": "<|reserved_special_token_4|>",
|
| 102 |
+
"lstrip": false,
|
| 103 |
+
"normalized": false,
|
| 104 |
+
"rstrip": false,
|
| 105 |
+
"single_word": false,
|
| 106 |
+
"special": true
|
| 107 |
+
},
|
| 108 |
+
"128013": {
|
| 109 |
+
"content": "<|reserved_special_token_5|>",
|
| 110 |
+
"lstrip": false,
|
| 111 |
+
"normalized": false,
|
| 112 |
+
"rstrip": false,
|
| 113 |
+
"single_word": false,
|
| 114 |
+
"special": true
|
| 115 |
+
},
|
| 116 |
+
"128014": {
|
| 117 |
+
"content": "<|reserved_special_token_6|>",
|
| 118 |
+
"lstrip": false,
|
| 119 |
+
"normalized": false,
|
| 120 |
+
"rstrip": false,
|
| 121 |
+
"single_word": false,
|
| 122 |
+
"special": true
|
| 123 |
+
},
|
| 124 |
+
"128015": {
|
| 125 |
+
"content": "<|reserved_special_token_7|>",
|
| 126 |
+
"lstrip": false,
|
| 127 |
+
"normalized": false,
|
| 128 |
+
"rstrip": false,
|
| 129 |
+
"single_word": false,
|
| 130 |
+
"special": true
|
| 131 |
+
},
|
| 132 |
+
"128016": {
|
| 133 |
+
"content": "<|reserved_special_token_8|>",
|
| 134 |
+
"lstrip": false,
|
| 135 |
+
"normalized": false,
|
| 136 |
+
"rstrip": false,
|
| 137 |
+
"single_word": false,
|
| 138 |
+
"special": true
|
| 139 |
+
},
|
| 140 |
+
"128017": {
|
| 141 |
+
"content": "<|reserved_special_token_9|>",
|
| 142 |
+
"lstrip": false,
|
| 143 |
+
"normalized": false,
|
| 144 |
+
"rstrip": false,
|
| 145 |
+
"single_word": false,
|
| 146 |
+
"special": true
|
| 147 |
+
},
|
| 148 |
+
"128018": {
|
| 149 |
+
"content": "<|reserved_special_token_10|>",
|
| 150 |
+
"lstrip": false,
|
| 151 |
+
"normalized": false,
|
| 152 |
+
"rstrip": false,
|
| 153 |
+
"single_word": false,
|
| 154 |
+
"special": true
|
| 155 |
+
},
|
| 156 |
+
"128019": {
|
| 157 |
+
"content": "<|reserved_special_token_11|>",
|
| 158 |
+
"lstrip": false,
|
| 159 |
+
"normalized": false,
|
| 160 |
+
"rstrip": false,
|
| 161 |
+
"single_word": false,
|
| 162 |
+
"special": true
|
| 163 |
+
},
|
| 164 |
+
"128020": {
|
| 165 |
+
"content": "<|reserved_special_token_12|>",
|
| 166 |
+
"lstrip": false,
|
| 167 |
+
"normalized": false,
|
| 168 |
+
"rstrip": false,
|
| 169 |
+
"single_word": false,
|
| 170 |
+
"special": true
|
| 171 |
+
},
|
| 172 |
+
"128021": {
|
| 173 |
+
"content": "<|reserved_special_token_13|>",
|
| 174 |
+
"lstrip": false,
|
| 175 |
+
"normalized": false,
|
| 176 |
+
"rstrip": false,
|
| 177 |
+
"single_word": false,
|
| 178 |
+
"special": true
|
| 179 |
+
},
|
| 180 |
+
"128022": {
|
| 181 |
+
"content": "<|reserved_special_token_14|>",
|
| 182 |
+
"lstrip": false,
|
| 183 |
+
"normalized": false,
|
| 184 |
+
"rstrip": false,
|
| 185 |
+
"single_word": false,
|
| 186 |
+
"special": true
|
| 187 |
+
},
|
| 188 |
+
"128023": {
|
| 189 |
+
"content": "<|reserved_special_token_15|>",
|
| 190 |
+
"lstrip": false,
|
| 191 |
+
"normalized": false,
|
| 192 |
+
"rstrip": false,
|
| 193 |
+
"single_word": false,
|
| 194 |
+
"special": true
|
| 195 |
+
},
|
| 196 |
+
"128024": {
|
| 197 |
+
"content": "<|reserved_special_token_16|>",
|
| 198 |
+
"lstrip": false,
|
| 199 |
+
"normalized": false,
|
| 200 |
+
"rstrip": false,
|
| 201 |
+
"single_word": false,
|
| 202 |
+
"special": true
|
| 203 |
+
},
|
| 204 |
+
"128025": {
|
| 205 |
+
"content": "<|reserved_special_token_17|>",
|
| 206 |
+
"lstrip": false,
|
| 207 |
+
"normalized": false,
|
| 208 |
+
"rstrip": false,
|
| 209 |
+
"single_word": false,
|
| 210 |
+
"special": true
|
| 211 |
+
},
|
| 212 |
+
"128026": {
|
| 213 |
+
"content": "<|reserved_special_token_18|>",
|
| 214 |
+
"lstrip": false,
|
| 215 |
+
"normalized": false,
|
| 216 |
+
"rstrip": false,
|
| 217 |
+
"single_word": false,
|
| 218 |
+
"special": true
|
| 219 |
+
},
|
| 220 |
+
"128027": {
|
| 221 |
+
"content": "<|reserved_special_token_19|>",
|
| 222 |
+
"lstrip": false,
|
| 223 |
+
"normalized": false,
|
| 224 |
+
"rstrip": false,
|
| 225 |
+
"single_word": false,
|
| 226 |
+
"special": true
|
| 227 |
+
},
|
| 228 |
+
"128028": {
|
| 229 |
+
"content": "<|reserved_special_token_20|>",
|
| 230 |
+
"lstrip": false,
|
| 231 |
+
"normalized": false,
|
| 232 |
+
"rstrip": false,
|
| 233 |
+
"single_word": false,
|
| 234 |
+
"special": true
|
| 235 |
+
},
|
| 236 |
+
"128029": {
|
| 237 |
+
"content": "<|reserved_special_token_21|>",
|
| 238 |
+
"lstrip": false,
|
| 239 |
+
"normalized": false,
|
| 240 |
+
"rstrip": false,
|
| 241 |
+
"single_word": false,
|
| 242 |
+
"special": true
|
| 243 |
+
},
|
| 244 |
+
"128030": {
|
| 245 |
+
"content": "<|reserved_special_token_22|>",
|
| 246 |
+
"lstrip": false,
|
| 247 |
+
"normalized": false,
|
| 248 |
+
"rstrip": false,
|
| 249 |
+
"single_word": false,
|
| 250 |
+
"special": true
|
| 251 |
+
},
|
| 252 |
+
"128031": {
|
| 253 |
+
"content": "<|reserved_special_token_23|>",
|
| 254 |
+
"lstrip": false,
|
| 255 |
+
"normalized": false,
|
| 256 |
+
"rstrip": false,
|
| 257 |
+
"single_word": false,
|
| 258 |
+
"special": true
|
| 259 |
+
},
|
| 260 |
+
"128032": {
|
| 261 |
+
"content": "<|reserved_special_token_24|>",
|
| 262 |
+
"lstrip": false,
|
| 263 |
+
"normalized": false,
|
| 264 |
+
"rstrip": false,
|
| 265 |
+
"single_word": false,
|
| 266 |
+
"special": true
|
| 267 |
+
},
|
| 268 |
+
"128033": {
|
| 269 |
+
"content": "<|reserved_special_token_25|>",
|
| 270 |
+
"lstrip": false,
|
| 271 |
+
"normalized": false,
|
| 272 |
+
"rstrip": false,
|
| 273 |
+
"single_word": false,
|
| 274 |
+
"special": true
|
| 275 |
+
},
|
| 276 |
+
"128034": {
|
| 277 |
+
"content": "<|reserved_special_token_26|>",
|
| 278 |
+
"lstrip": false,
|
| 279 |
+
"normalized": false,
|
| 280 |
+
"rstrip": false,
|
| 281 |
+
"single_word": false,
|
| 282 |
+
"special": true
|
| 283 |
+
},
|
| 284 |
+
"128035": {
|
| 285 |
+
"content": "<|reserved_special_token_27|>",
|
| 286 |
+
"lstrip": false,
|
| 287 |
+
"normalized": false,
|
| 288 |
+
"rstrip": false,
|
| 289 |
+
"single_word": false,
|
| 290 |
+
"special": true
|
| 291 |
+
},
|
| 292 |
+
"128036": {
|
| 293 |
+
"content": "<|reserved_special_token_28|>",
|
| 294 |
+
"lstrip": false,
|
| 295 |
+
"normalized": false,
|
| 296 |
+
"rstrip": false,
|
| 297 |
+
"single_word": false,
|
| 298 |
+
"special": true
|
| 299 |
+
},
|
| 300 |
+
"128037": {
|
| 301 |
+
"content": "<|reserved_special_token_29|>",
|
| 302 |
+
"lstrip": false,
|
| 303 |
+
"normalized": false,
|
| 304 |
+
"rstrip": false,
|
| 305 |
+
"single_word": false,
|
| 306 |
+
"special": true
|
| 307 |
+
},
|
| 308 |
+
"128038": {
|
| 309 |
+
"content": "<|reserved_special_token_30|>",
|
| 310 |
+
"lstrip": false,
|
| 311 |
+
"normalized": false,
|
| 312 |
+
"rstrip": false,
|
| 313 |
+
"single_word": false,
|
| 314 |
+
"special": true
|
| 315 |
+
},
|
| 316 |
+
"128039": {
|
| 317 |
+
"content": "<|reserved_special_token_31|>",
|
| 318 |
+
"lstrip": false,
|
| 319 |
+
"normalized": false,
|
| 320 |
+
"rstrip": false,
|
| 321 |
+
"single_word": false,
|
| 322 |
+
"special": true
|
| 323 |
+
},
|
| 324 |
+
"128040": {
|
| 325 |
+
"content": "<|reserved_special_token_32|>",
|
| 326 |
+
"lstrip": false,
|
| 327 |
+
"normalized": false,
|
| 328 |
+
"rstrip": false,
|
| 329 |
+
"single_word": false,
|
| 330 |
+
"special": true
|
| 331 |
+
},
|
| 332 |
+
"128041": {
|
| 333 |
+
"content": "<|reserved_special_token_33|>",
|
| 334 |
+
"lstrip": false,
|
| 335 |
+
"normalized": false,
|
| 336 |
+
"rstrip": false,
|
| 337 |
+
"single_word": false,
|
| 338 |
+
"special": true
|
| 339 |
+
},
|
| 340 |
+
"128042": {
|
| 341 |
+
"content": "<|reserved_special_token_34|>",
|
| 342 |
+
"lstrip": false,
|
| 343 |
+
"normalized": false,
|
| 344 |
+
"rstrip": false,
|
| 345 |
+
"single_word": false,
|
| 346 |
+
"special": true
|
| 347 |
+
},
|
| 348 |
+
"128043": {
|
| 349 |
+
"content": "<|reserved_special_token_35|>",
|
| 350 |
+
"lstrip": false,
|
| 351 |
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"normalized": false,
|
| 352 |
+
"rstrip": false,
|
| 353 |
+
"single_word": false,
|
| 354 |
+
"special": true
|
| 355 |
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},
|
| 356 |
+
"128044": {
|
| 357 |
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"content": "<|reserved_special_token_36|>",
|
| 358 |
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"lstrip": false,
|
| 359 |
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"normalized": false,
|
| 360 |
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"rstrip": false,
|
| 361 |
+
"single_word": false,
|
| 362 |
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"special": true
|
| 363 |
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},
|
| 364 |
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"128045": {
|
| 365 |
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"content": "<|reserved_special_token_37|>",
|
| 366 |
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"lstrip": false,
|
| 367 |
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"normalized": false,
|
| 368 |
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"rstrip": false,
|
| 369 |
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"single_word": false,
|
| 370 |
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"special": true
|
| 371 |
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},
|
| 372 |
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"128046": {
|
| 373 |
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"content": "<|reserved_special_token_38|>",
|
| 374 |
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"lstrip": false,
|
| 375 |
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| 1830 |
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| 1851 |
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|
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| 1861 |
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| 1862 |
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|
| 1867 |
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|
| 1868 |
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|
| 1869 |
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|
| 1870 |
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|
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|
| 1878 |
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|
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| 1882 |
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|
| 1883 |
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|
| 1884 |
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|
| 1885 |
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|
| 1886 |
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|
| 1887 |
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|
| 1888 |
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| 1890 |
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|
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|
| 1892 |
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|
| 1893 |
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|
| 1894 |
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| 1895 |
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| 1898 |
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| 1899 |
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|
| 1901 |
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| 1902 |
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|
| 1907 |
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| 1909 |
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|
| 1910 |
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| 1911 |
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| 1914 |
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|
| 1915 |
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| 1916 |
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|
| 1917 |
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|
| 1918 |
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| 1919 |
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|
| 1920 |
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| 1921 |
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| 1922 |
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|
| 1923 |
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|
| 1924 |
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|
| 1925 |
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|
| 1926 |
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| 1927 |
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|
| 1928 |
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| 1929 |
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|
| 1930 |
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|
| 1931 |
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| 1932 |
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|
| 1933 |
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|
| 1934 |
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| 1935 |
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|
| 1936 |
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| 1937 |
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| 1938 |
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|
| 1939 |
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|
| 1940 |
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|
| 1941 |
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|
| 1942 |
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| 1943 |
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|
| 1944 |
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| 1945 |
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| 1946 |
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|
| 1947 |
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|
| 1948 |
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|
| 1949 |
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|
| 1950 |
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|
| 1951 |
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| 1952 |
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| 1953 |
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| 1954 |
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|
| 1955 |
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|
| 1956 |
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|
| 1957 |
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|
| 1958 |
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| 1959 |
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|
| 1960 |
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| 1961 |
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| 1962 |
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|
| 1963 |
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|
| 1964 |
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|
| 1965 |
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|
| 1966 |
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| 1967 |
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|
| 1968 |
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| 1969 |
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|
| 1970 |
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|
| 1971 |
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|
| 1972 |
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|
| 1973 |
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|
| 1974 |
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| 1975 |
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|
| 1976 |
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| 1977 |
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|
| 1978 |
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|
| 1979 |
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|
| 1980 |
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|
| 1981 |
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|
| 1982 |
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|
| 1983 |
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|
| 1984 |
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| 1985 |
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|
| 1986 |
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|
| 1987 |
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|
| 1988 |
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|
| 1989 |
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"content": "<|reserved_special_token_240|>",
|
| 1990 |
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|
| 1991 |
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|
| 1992 |
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|
| 1993 |
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| 1994 |
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|
| 1995 |
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|
| 1996 |
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|
| 1997 |
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|
| 1998 |
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| 1999 |
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|
| 2000 |
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| 2001 |
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| 2002 |
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|
| 2003 |
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|
| 2004 |
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|
| 2005 |
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| 2006 |
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| 2009 |
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| 2010 |
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|
| 2011 |
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|
| 2012 |
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|
| 2013 |
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|
| 2014 |
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| 2015 |
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|
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| 2017 |
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|
| 2018 |
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"special": true
|
| 2019 |
+
},
|
| 2020 |
+
"128252": {
|
| 2021 |
+
"content": "<|reserved_special_token_244|>",
|
| 2022 |
+
"lstrip": false,
|
| 2023 |
+
"normalized": false,
|
| 2024 |
+
"rstrip": false,
|
| 2025 |
+
"single_word": false,
|
| 2026 |
+
"special": true
|
| 2027 |
+
},
|
| 2028 |
+
"128253": {
|
| 2029 |
+
"content": "<|reserved_special_token_245|>",
|
| 2030 |
+
"lstrip": false,
|
| 2031 |
+
"normalized": false,
|
| 2032 |
+
"rstrip": false,
|
| 2033 |
+
"single_word": false,
|
| 2034 |
+
"special": true
|
| 2035 |
+
},
|
| 2036 |
+
"128254": {
|
| 2037 |
+
"content": "<|reserved_special_token_246|>",
|
| 2038 |
+
"lstrip": false,
|
| 2039 |
+
"normalized": false,
|
| 2040 |
+
"rstrip": false,
|
| 2041 |
+
"single_word": false,
|
| 2042 |
+
"special": true
|
| 2043 |
+
},
|
| 2044 |
+
"128255": {
|
| 2045 |
+
"content": "<|reserved_special_token_247|>",
|
| 2046 |
+
"lstrip": false,
|
| 2047 |
+
"normalized": false,
|
| 2048 |
+
"rstrip": false,
|
| 2049 |
+
"single_word": false,
|
| 2050 |
+
"special": true
|
| 2051 |
+
}
|
| 2052 |
+
},
|
| 2053 |
+
"bos_token": "<|begin_of_text|>",
|
| 2054 |
+
"clean_up_tokenization_spaces": true,
|
| 2055 |
+
"eos_token": "<|end_of_text|>",
|
| 2056 |
+
"extra_special_tokens": {},
|
| 2057 |
+
"model_input_names": [
|
| 2058 |
+
"input_ids",
|
| 2059 |
+
"attention_mask"
|
| 2060 |
+
],
|
| 2061 |
+
"model_max_length": 131072,
|
| 2062 |
+
"pad_token": "<|finetune_right_pad_id|>",
|
| 2063 |
+
"padding_side": "left",
|
| 2064 |
+
"tokenizer_class": "PreTrainedTokenizerFast",
|
| 2065 |
+
"unk_token": null
|
| 2066 |
+
}
|
models/v10/config.json
ADDED
|
@@ -0,0 +1,36 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "models/v10",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"LlamaForCausalLM"
|
| 5 |
+
],
|
| 6 |
+
"attention_bias": false,
|
| 7 |
+
"attention_dropout": 0.0,
|
| 8 |
+
"bos_token_id": 133309,
|
| 9 |
+
"eos_token_id": 133310,
|
| 10 |
+
"head_dim": 64,
|
| 11 |
+
"hidden_act": "silu",
|
| 12 |
+
"hidden_size": 2048,
|
| 13 |
+
"initializer_range": 0.02,
|
| 14 |
+
"intermediate_size": 8192,
|
| 15 |
+
"max_position_embeddings": 131072,
|
| 16 |
+
"mlp_bias": false,
|
| 17 |
+
"model_type": "llama",
|
| 18 |
+
"num_attention_heads": 32,
|
| 19 |
+
"num_hidden_layers": 16,
|
| 20 |
+
"num_key_value_heads": 8,
|
| 21 |
+
"pretraining_tp": 1,
|
| 22 |
+
"rms_norm_eps": 1e-05,
|
| 23 |
+
"rope_scaling": {
|
| 24 |
+
"factor": 32.0,
|
| 25 |
+
"high_freq_factor": 4.0,
|
| 26 |
+
"low_freq_factor": 1.0,
|
| 27 |
+
"original_max_position_embeddings": 8192,
|
| 28 |
+
"rope_type": "llama3"
|
| 29 |
+
},
|
| 30 |
+
"rope_theta": 500000.0,
|
| 31 |
+
"tie_word_embeddings": true,
|
| 32 |
+
"torch_dtype": "bfloat16",
|
| 33 |
+
"transformers_version": "4.48.0",
|
| 34 |
+
"use_cache": true,
|
| 35 |
+
"vocab_size": 134400
|
| 36 |
+
}
|
models/v10/generation_config.json
ADDED
|
@@ -0,0 +1,13 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"bos_token_id": 133309,
|
| 4 |
+
"do_sample": true,
|
| 5 |
+
"eos_token_id": 133310,
|
| 6 |
+
"min_p": 0.05,
|
| 7 |
+
"pad_token_id": 128001,
|
| 8 |
+
"repetition_penalty": 1.1,
|
| 9 |
+
"temperature": 0.4,
|
| 10 |
+
"top_k": 40,
|
| 11 |
+
"top_p": 0.9,
|
| 12 |
+
"transformers_version": "4.48.0"
|
| 13 |
+
}
|
models/v10/model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:30f3df2b3999f0cf34cb2a1a9c84ce0b2b96e8717019a0a76d8cb00c81a86da7
|
| 3 |
+
size 2496811440
|
models/v10/special_tokens_map.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
models/v10/tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ec54a55e3c6fc7318ea02cbd1a6eb1fb180bff1b58acbf068504a25f1b407b7b
|
| 3 |
+
size 18366636
|
models/v10/tokenizer_config.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
models/wpt/wpt.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794
|
| 3 |
+
size 483617219
|
requirements.txt
ADDED
|
@@ -0,0 +1,13 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
transformers==4.48.3
|
| 2 |
+
pydantic==2.11.4
|
| 3 |
+
numpy==2.2.5
|
| 4 |
+
torch==2.6.0
|
| 5 |
+
torchaudio==2.6.0
|
| 6 |
+
torchvision==0.21.0
|
| 7 |
+
outetts==0.4.1
|
| 8 |
+
fastapi==0.115.12
|
| 9 |
+
uvicorn==0.34.2
|
| 10 |
+
librosa==0.11.0
|
| 11 |
+
openai-whisper==20240930
|
| 12 |
+
soundfile==0.13.1
|
| 13 |
+
accelerate==0.26.0
|
server.py
ADDED
|
@@ -0,0 +1,224 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from fastapi import FastAPI, HTTPException
|
| 2 |
+
from fastapi.middleware.cors import CORSMiddleware
|
| 3 |
+
from pydantic import BaseModel, Field
|
| 4 |
+
from pathlib import Path
|
| 5 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer
|
| 6 |
+
import tempfile
|
| 7 |
+
import traceback
|
| 8 |
+
import whisper
|
| 9 |
+
import librosa
|
| 10 |
+
import numpy as np
|
| 11 |
+
import torch
|
| 12 |
+
import outetts
|
| 13 |
+
import uvicorn
|
| 14 |
+
import base64
|
| 15 |
+
import io
|
| 16 |
+
import soundfile as sf
|
| 17 |
+
|
| 18 |
+
try:
|
| 19 |
+
INTERFACE = outetts.Interface(
|
| 20 |
+
config=outetts.ModelConfig(
|
| 21 |
+
model_path="models/v10",
|
| 22 |
+
tokenizer_path="models/v10",
|
| 23 |
+
audio_codec_path="models/dsp/weights_24khz_1.5kbps_v1.0.pth",
|
| 24 |
+
device="cuda",
|
| 25 |
+
dtype=torch.bfloat16,
|
| 26 |
+
)
|
| 27 |
+
)
|
| 28 |
+
except Exception as e:
|
| 29 |
+
raise RuntimeError(f"{e}")
|
| 30 |
+
|
| 31 |
+
asr_model = whisper.load_model("models/wpt/wpt.pt")
|
| 32 |
+
model_name = "/home/salman/salman/minomni_sn21/omega-v2v/miner_models/Llama-3.2-1B-Instruct"
|
| 33 |
+
tok = AutoTokenizer.from_pretrained(model_name)
|
| 34 |
+
lm = AutoModelForCausalLM.from_pretrained(
|
| 35 |
+
model_name,
|
| 36 |
+
torch_dtype=torch.bfloat16,
|
| 37 |
+
device_map="cuda",
|
| 38 |
+
).eval()
|
| 39 |
+
SPEAKER_WAV_PATH = Path(__file__).with_name("spk_001.wav")
|
| 40 |
+
|
| 41 |
+
|
| 42 |
+
def chat(system_prompt: str, user_prompt: str) -> str:
|
| 43 |
+
"""
|
| 44 |
+
Run one turn of chat with a system + user message.
|
| 45 |
+
Extra **gen_kwargs are forwarded to `generate()`.
|
| 46 |
+
"""
|
| 47 |
+
messages = [
|
| 48 |
+
{"role": "system", "content": system_prompt},
|
| 49 |
+
{"role": "user", "content": user_prompt},
|
| 50 |
+
]
|
| 51 |
+
|
| 52 |
+
# `add_generation_prompt=True` automatically appends the
|
| 53 |
+
# <|start_header_id|>assistant … header so the model knows to respond.
|
| 54 |
+
input_ids = tok.apply_chat_template(
|
| 55 |
+
messages,
|
| 56 |
+
add_generation_prompt=True,
|
| 57 |
+
return_tensors="pt"
|
| 58 |
+
).to(lm.device)
|
| 59 |
+
|
| 60 |
+
with torch.inference_mode():
|
| 61 |
+
output_ids = lm.generate(
|
| 62 |
+
input_ids,
|
| 63 |
+
max_new_tokens=2048,
|
| 64 |
+
do_sample=True,
|
| 65 |
+
temperature=0.2,
|
| 66 |
+
repetition_penalty=1.1,
|
| 67 |
+
top_k=100,
|
| 68 |
+
top_p=0.95,
|
| 69 |
+
)
|
| 70 |
+
|
| 71 |
+
# Strip the prompt part and return only the newly-generated answer
|
| 72 |
+
answer = tok.decode(
|
| 73 |
+
output_ids[0][input_ids.shape[-1]:],
|
| 74 |
+
skip_special_tokens=True,
|
| 75 |
+
clean_up_tokenization_spaces=True,
|
| 76 |
+
)
|
| 77 |
+
return answer.strip()
|
| 78 |
+
|
| 79 |
+
def gt(audio: np.ndarray, sr: int):
|
| 80 |
+
ss = audio.squeeze().astype(np.float32)
|
| 81 |
+
if sr != 16_000:
|
| 82 |
+
ss = librosa.resample(audio, orig_sr=sr, target_sr=16_000)
|
| 83 |
+
|
| 84 |
+
result = asr_model.transcribe(ss, fp16=False, language=None)
|
| 85 |
+
return result["text"].strip()
|
| 86 |
+
|
| 87 |
+
|
| 88 |
+
def sample(rr: str) -> str:
|
| 89 |
+
if rr.strip() == "":
|
| 90 |
+
rr = "Hello "
|
| 91 |
+
|
| 92 |
+
inputs = tok(rr, return_tensors="pt").to(lm.device)
|
| 93 |
+
|
| 94 |
+
with torch.inference_mode():
|
| 95 |
+
out_ids = lm.generate(
|
| 96 |
+
**inputs,
|
| 97 |
+
max_new_tokens=2048,
|
| 98 |
+
do_sample=True,
|
| 99 |
+
temperature=0.2,
|
| 100 |
+
repetition_penalty=1.1,
|
| 101 |
+
top_k=100,
|
| 102 |
+
top_p=0.95,
|
| 103 |
+
)
|
| 104 |
+
|
| 105 |
+
return tok.decode(
|
| 106 |
+
out_ids[0][inputs.input_ids.shape[-1] :], skip_special_tokens=True
|
| 107 |
+
)
|
| 108 |
+
|
| 109 |
+
|
| 110 |
+
INITIALIZATION_STATUS = {"model_loaded": True, "error": None}
|
| 111 |
+
|
| 112 |
+
|
| 113 |
+
class GenerateRequest(BaseModel):
|
| 114 |
+
audio_data: str = Field(
|
| 115 |
+
...,
|
| 116 |
+
description="",
|
| 117 |
+
)
|
| 118 |
+
sample_rate: int = Field(..., description="")
|
| 119 |
+
|
| 120 |
+
|
| 121 |
+
class GenerateResponse(BaseModel):
|
| 122 |
+
audio_data: str = Field(..., description="")
|
| 123 |
+
|
| 124 |
+
|
| 125 |
+
app = FastAPI(title="V1", version="0.1")
|
| 126 |
+
|
| 127 |
+
app.add_middleware(
|
| 128 |
+
CORSMiddleware,
|
| 129 |
+
allow_origins=["*"],
|
| 130 |
+
allow_credentials=True,
|
| 131 |
+
allow_methods=["*"],
|
| 132 |
+
allow_headers=["*"],
|
| 133 |
+
)
|
| 134 |
+
|
| 135 |
+
|
| 136 |
+
def b64(b64: str) -> np.ndarray:
|
| 137 |
+
raw = base64.b64decode(b64)
|
| 138 |
+
return np.load(io.BytesIO(raw), allow_pickle=False)
|
| 139 |
+
|
| 140 |
+
|
| 141 |
+
def ab64(arr: np.ndarray, sr: int) -> str:
|
| 142 |
+
buf = io.BytesIO()
|
| 143 |
+
resampled = librosa.resample(arr, orig_sr=44100, target_sr=sr)
|
| 144 |
+
np.save(buf, resampled.astype(np.float32))
|
| 145 |
+
return base64.b64encode(buf.getvalue()).decode()
|
| 146 |
+
|
| 147 |
+
|
| 148 |
+
def gs(
|
| 149 |
+
audio: np.ndarray,
|
| 150 |
+
sr: int,
|
| 151 |
+
interface: outetts.Interface,
|
| 152 |
+
):
|
| 153 |
+
if audio.ndim == 2:
|
| 154 |
+
audio = audio.squeeze()
|
| 155 |
+
audio = audio.astype("float32")
|
| 156 |
+
max_samples = int(15.0 * sr)
|
| 157 |
+
if audio.shape[-1] > max_samples:
|
| 158 |
+
audio = audio[-max_samples:]
|
| 159 |
+
|
| 160 |
+
with tempfile.NamedTemporaryFile(suffix=".wav", dir="/tmp", delete=False) as f:
|
| 161 |
+
sf.write(f.name, audio, sr)
|
| 162 |
+
speaker = interface.create_speaker(
|
| 163 |
+
f.name,
|
| 164 |
+
whisper_model="models/wpt/wpt.pt",
|
| 165 |
+
)
|
| 166 |
+
|
| 167 |
+
return speaker
|
| 168 |
+
|
| 169 |
+
|
| 170 |
+
@app.get("/api/v1/health")
|
| 171 |
+
def health_check():
|
| 172 |
+
"""Health check endpoint"""
|
| 173 |
+
status = {
|
| 174 |
+
"status": "healthy",
|
| 175 |
+
"model_loaded": INITIALIZATION_STATUS["model_loaded"],
|
| 176 |
+
"error": INITIALIZATION_STATUS["error"],
|
| 177 |
+
}
|
| 178 |
+
return status
|
| 179 |
+
|
| 180 |
+
|
| 181 |
+
@app.post("/api/v1/inference", response_model=GenerateResponse)
|
| 182 |
+
def generate_audio(req: GenerateRequest):
|
| 183 |
+
audio_np = b64(req.audio_data)
|
| 184 |
+
if audio_np.ndim == 1:
|
| 185 |
+
audio_np = audio_np.reshape(1, -1)
|
| 186 |
+
|
| 187 |
+
try:
|
| 188 |
+
text = gt(audio_np, req.sample_rate)
|
| 189 |
+
out = INTERFACE.generate(
|
| 190 |
+
config=outetts.GenerationConfig(
|
| 191 |
+
text=sample(text),
|
| 192 |
+
generation_type=outetts.GenerationType.CHUNKED,
|
| 193 |
+
speaker=gs(audio_np, req.sample_rate, INTERFACE),
|
| 194 |
+
sampler_config=outetts.SamplerConfig(),
|
| 195 |
+
)
|
| 196 |
+
)
|
| 197 |
+
audio_out = out.audio.squeeze().cpu().numpy()
|
| 198 |
+
except Exception as e:
|
| 199 |
+
traceback.print_exc()
|
| 200 |
+
raise HTTPException(status_code=500, detail=f"{e}")
|
| 201 |
+
|
| 202 |
+
return GenerateResponse(audio_data=ab64(audio_out, req.sample_rate))
|
| 203 |
+
|
| 204 |
+
@app.post("/api/v1/v2t")
|
| 205 |
+
def generate_text(req: GenerateRequest):
|
| 206 |
+
audio_np = b64(req.audio_data)
|
| 207 |
+
if audio_np.ndim == 1:
|
| 208 |
+
audio_np = audio_np.reshape(1, -1)
|
| 209 |
+
|
| 210 |
+
try:
|
| 211 |
+
text = gt(audio_np, req.sample_rate)
|
| 212 |
+
print(f"Transcribed text: {text}")
|
| 213 |
+
# response_text = sample(text)
|
| 214 |
+
system_prompt = "You are a helpful assistant who tries to help answer the user's question."
|
| 215 |
+
response_text = chat(system_prompt, user_prompt=text)
|
| 216 |
+
except Exception as e:
|
| 217 |
+
traceback.print_exc()
|
| 218 |
+
raise HTTPException(status_code=500, detail=f"{e}")
|
| 219 |
+
|
| 220 |
+
return {"text": response_text}
|
| 221 |
+
|
| 222 |
+
|
| 223 |
+
if __name__ == "__main__":
|
| 224 |
+
uvicorn.run("server:app", host="0.0.0.0", port=8010, reload=False)
|
spk_001.wav
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:79de3a5775f8880c0bf3e950b103f03b257db630224fab265a309d82753b1aa5
|
| 3 |
+
size 480044
|
test.ipynb
ADDED
|
@@ -0,0 +1,190 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 1,
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"outputs": [
|
| 8 |
+
{
|
| 9 |
+
"name": "stderr",
|
| 10 |
+
"output_type": "stream",
|
| 11 |
+
"text": [
|
| 12 |
+
"/home/salman/salman/minomni_sn21/omega-v2v/console/backend/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
|
| 13 |
+
" from .autonotebook import tqdm as notebook_tqdm\n",
|
| 14 |
+
"/home/salman/salman/minomni_sn21/omega-v2v/console/backend/venv/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.\n",
|
| 15 |
+
" WeightNorm.apply(module, name, dim)\n"
|
| 16 |
+
]
|
| 17 |
+
}
|
| 18 |
+
],
|
| 19 |
+
"source": [
|
| 20 |
+
"from server import lm"
|
| 21 |
+
]
|
| 22 |
+
},
|
| 23 |
+
{
|
| 24 |
+
"cell_type": "code",
|
| 25 |
+
"execution_count": 2,
|
| 26 |
+
"metadata": {},
|
| 27 |
+
"outputs": [],
|
| 28 |
+
"source": [
|
| 29 |
+
"from server import tok"
|
| 30 |
+
]
|
| 31 |
+
},
|
| 32 |
+
{
|
| 33 |
+
"cell_type": "code",
|
| 34 |
+
"execution_count": 3,
|
| 35 |
+
"metadata": {},
|
| 36 |
+
"outputs": [],
|
| 37 |
+
"source": [
|
| 38 |
+
"import torch"
|
| 39 |
+
]
|
| 40 |
+
},
|
| 41 |
+
{
|
| 42 |
+
"cell_type": "code",
|
| 43 |
+
"execution_count": 4,
|
| 44 |
+
"metadata": {},
|
| 45 |
+
"outputs": [
|
| 46 |
+
{
|
| 47 |
+
"name": "stderr",
|
| 48 |
+
"output_type": "stream",
|
| 49 |
+
"text": [
|
| 50 |
+
"\u001b[32m2025-07-17 20:59:03.022\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36moutetts.models.hf_model\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m20\u001b[0m - \u001b[1m🔄 Using patched RepetitionPenaltyLogitsProcessor -> RepetitionPenaltyLogitsProcessorPatch | penalty_last_n: 64\u001b[0m\n"
|
| 51 |
+
]
|
| 52 |
+
}
|
| 53 |
+
],
|
| 54 |
+
"source": [
|
| 55 |
+
"\n",
|
| 56 |
+
"rr = \"\"\"I'm trying to come up with a funny name for my new goldfish. He's orange with a white spot on his head and he's pretty energetic. Got any silly suggestions?\"\"\"\n",
|
| 57 |
+
"\n",
|
| 58 |
+
"inputs = tok(rr, return_tensors=\"pt\").to(lm.device)\n",
|
| 59 |
+
"\n",
|
| 60 |
+
"with torch.inference_mode():\n",
|
| 61 |
+
" out_ids = lm.generate(\n",
|
| 62 |
+
" **inputs,\n",
|
| 63 |
+
" max_new_tokens=500,\n",
|
| 64 |
+
" do_sample=True,\n",
|
| 65 |
+
" temperature=0.2,\n",
|
| 66 |
+
" repetition_penalty=1.11,\n",
|
| 67 |
+
" top_k=100,\n",
|
| 68 |
+
" top_p=0.95,\n",
|
| 69 |
+
" )\n",
|
| 70 |
+
"\n",
|
| 71 |
+
"resp = tok.decode(\n",
|
| 72 |
+
" out_ids[0][inputs.input_ids.shape[-1] :], skip_special_tokens=True\n",
|
| 73 |
+
" )"
|
| 74 |
+
]
|
| 75 |
+
},
|
| 76 |
+
{
|
| 77 |
+
"cell_type": "code",
|
| 78 |
+
"execution_count": 5,
|
| 79 |
+
"metadata": {},
|
| 80 |
+
"outputs": [
|
| 81 |
+
{
|
| 82 |
+
"data": {
|
| 83 |
+
"text/plain": [
|
| 84 |
+
"\" I've got a few, but they aren't very catchy. The one I like the best is just gonna be called fish. It's kinda long and it's kinda boring. Oh, I thought you were gonna give me some name for the goldfish. I'm just kidding. Yeah. So, you know, it's really easy to take care of a goldfish. We have a big tank, and, we're both in the same house. So it's not like, oh, where are my three goldfish? You know, it's just, oh, how many goldfish do you have? It's, like, four or five. But, we only have room for one person to be a goldfish keeper. So that is hard, especially when it's, like, 20 degrees outside and you're trying to keep a fish at home. Right? Yeah. That's difficult. And with the tank being this size, you don't really feel bad about taking him out. You know, you just kinda get a little more nervous because you know you're gonna be doing a big fish transfer if you have that big of a tank and all that stuff. But Mhmm. It's much easier to take care of the goldfish at home. So I wouldFor the rest of us simple folks, we worry about somebody stealing our password. To you, you laugh about it because you know how to do that with your eyes closed, right, with the technology you've created. So nowadays, you talk to certain investors, so where do hide your passwords? I don't want to really say, but I hide my passwords in my notes section on my phone. Oh shoot. Okay. Where do you hide your passwords? I write it on a piece of paper. Where do you hide your password? I have it on file on my computer. Where do you hide your password? I have it on an Excel spreadsheet, right? And all these places you go through. And so now there's a business model for apps that you put your passwords in and they protect your password. If it's so easy to break into softwares to get my password, How can I trust an app to restore all my password? Is there anywhere you trust to restore your passwords? So let's imagine that I want your password. I'm gonna make a website for Iranian American fans of Atlas Shrugged, and I'm gonna send you an email with a,\""
|
| 85 |
+
]
|
| 86 |
+
},
|
| 87 |
+
"execution_count": 5,
|
| 88 |
+
"metadata": {},
|
| 89 |
+
"output_type": "execute_result"
|
| 90 |
+
}
|
| 91 |
+
],
|
| 92 |
+
"source": [
|
| 93 |
+
"resp"
|
| 94 |
+
]
|
| 95 |
+
},
|
| 96 |
+
{
|
| 97 |
+
"cell_type": "code",
|
| 98 |
+
"execution_count": 8,
|
| 99 |
+
"metadata": {},
|
| 100 |
+
"outputs": [
|
| 101 |
+
{
|
| 102 |
+
"data": {
|
| 103 |
+
"text/plain": [
|
| 104 |
+
"'All right. Good afternoon, everybody. Welcome to Friday afternoon. Appreciate you all coming. Really pleased today to be able to host the students to to COVID. Great. Correct me if I get it wrong. From the University of Wisconsin,'"
|
| 105 |
+
]
|
| 106 |
+
},
|
| 107 |
+
"execution_count": 8,
|
| 108 |
+
"metadata": {},
|
| 109 |
+
"output_type": "execute_result"
|
| 110 |
+
}
|
| 111 |
+
],
|
| 112 |
+
"source": [
|
| 113 |
+
"resp"
|
| 114 |
+
]
|
| 115 |
+
},
|
| 116 |
+
{
|
| 117 |
+
"cell_type": "code",
|
| 118 |
+
"execution_count": null,
|
| 119 |
+
"metadata": {},
|
| 120 |
+
"outputs": [],
|
| 121 |
+
"source": []
|
| 122 |
+
},
|
| 123 |
+
{
|
| 124 |
+
"cell_type": "code",
|
| 125 |
+
"execution_count": null,
|
| 126 |
+
"metadata": {},
|
| 127 |
+
"outputs": [
|
| 128 |
+
{
|
| 129 |
+
"ename": "ValueError",
|
| 130 |
+
"evalue": "Cannot use chat template functions because tokenizer.chat_template is not set and no template argument was passed! For information about writing templates and setting the tokenizer.chat_template attribute, please see the documentation at https://huggingface.co/docs/transformers/main/en/chat_templating",
|
| 131 |
+
"output_type": "error",
|
| 132 |
+
"traceback": [
|
| 133 |
+
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
| 134 |
+
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
|
| 135 |
+
"Cell \u001b[0;32mIn[6], line 5\u001b[0m\n\u001b[1;32m 1\u001b[0m messages \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 2\u001b[0m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msystem\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou are a concise assistant that answers in short paragraphs.\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m 3\u001b[0m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muser\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExplain rotary positional embeddings briefly.\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m 4\u001b[0m ]\n\u001b[0;32m----> 5\u001b[0m prompt_ids \u001b[38;5;241m=\u001b[39m \u001b[43mtok\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_chat_template\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43madd_generation_prompt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# appends the assistant header the model should complete\u001b[39;49;00m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_tensors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpt\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m 9\u001b[0m \u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mto(lm\u001b[38;5;241m.\u001b[39mdevice)\n",
|
| 136 |
+
"File \u001b[0;32m~/salman/minomni_sn21/omega-v2v/console/backend/venv/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1621\u001b[0m, in \u001b[0;36mPreTrainedTokenizerBase.apply_chat_template\u001b[0;34m(self, conversation, tools, documents, chat_template, add_generation_prompt, continue_final_message, tokenize, padding, truncation, max_length, return_tensors, return_dict, return_assistant_tokens_mask, tokenizer_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 1618\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tokenizer_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1619\u001b[0m tokenizer_kwargs \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m-> 1621\u001b[0m chat_template \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_chat_template\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchat_template\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtools\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1623\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_assistant_tokens_mask \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m re\u001b[38;5;241m.\u001b[39msearch(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124m{\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m-?\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms*generation\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms*-?\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124m}\u001b[39m\u001b[38;5;124m\"\u001b[39m, chat_template):\n\u001b[1;32m 1624\u001b[0m logger\u001b[38;5;241m.\u001b[39mwarning_once(\n\u001b[1;32m 1625\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreturn_assistant_tokens_mask==True but chat template does not contain `\u001b[39m\u001b[38;5;124m{\u001b[39m\u001b[38;5;132;01m% g\u001b[39;00m\u001b[38;5;124meneration \u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m}` keyword.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1626\u001b[0m )\n",
|
| 137 |
+
"File \u001b[0;32m~/salman/minomni_sn21/omega-v2v/console/backend/venv/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1789\u001b[0m, in \u001b[0;36mPreTrainedTokenizerBase.get_chat_template\u001b[0;34m(self, chat_template, tools)\u001b[0m\n\u001b[1;32m 1787\u001b[0m chat_template \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mchat_template\n\u001b[1;32m 1788\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1789\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 1790\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot use chat template functions because tokenizer.chat_template is not set and no template \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1791\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124margument was passed! For information about writing templates and setting the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1792\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtokenizer.chat_template attribute, please see the documentation at \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1793\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://huggingface.co/docs/transformers/main/en/chat_templating\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1794\u001b[0m )\n\u001b[1;32m 1796\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m chat_template\n",
|
| 138 |
+
"\u001b[0;31mValueError\u001b[0m: Cannot use chat template functions because tokenizer.chat_template is not set and no template argument was passed! For information about writing templates and setting the tokenizer.chat_template attribute, please see the documentation at https://huggingface.co/docs/transformers/main/en/chat_templating"
|
| 139 |
+
]
|
| 140 |
+
}
|
| 141 |
+
],
|
| 142 |
+
"source": [
|
| 143 |
+
"messages = [\n",
|
| 144 |
+
" {\"role\": \"system\", \"content\": \"You are a concise assistant that answers in short paragraphs.\"},\n",
|
| 145 |
+
" {\"role\": \"user\", \"content\": \"Explain rotary positional embeddings briefly.\"},\n",
|
| 146 |
+
"]\n",
|
| 147 |
+
"prompt_ids = tok.apply_chat_template(\n",
|
| 148 |
+
" messages,\n",
|
| 149 |
+
" add_generation_prompt=True, # appends the assistant header the model should complete\n",
|
| 150 |
+
" return_tensors=\"pt\"\n",
|
| 151 |
+
").to(lm.device)\n"
|
| 152 |
+
]
|
| 153 |
+
},
|
| 154 |
+
{
|
| 155 |
+
"cell_type": "code",
|
| 156 |
+
"execution_count": null,
|
| 157 |
+
"metadata": {},
|
| 158 |
+
"outputs": [],
|
| 159 |
+
"source": []
|
| 160 |
+
},
|
| 161 |
+
{
|
| 162 |
+
"cell_type": "code",
|
| 163 |
+
"execution_count": null,
|
| 164 |
+
"metadata": {},
|
| 165 |
+
"outputs": [],
|
| 166 |
+
"source": []
|
| 167 |
+
}
|
| 168 |
+
],
|
| 169 |
+
"metadata": {
|
| 170 |
+
"kernelspec": {
|
| 171 |
+
"display_name": "venv",
|
| 172 |
+
"language": "python",
|
| 173 |
+
"name": "python3"
|
| 174 |
+
},
|
| 175 |
+
"language_info": {
|
| 176 |
+
"codemirror_mode": {
|
| 177 |
+
"name": "ipython",
|
| 178 |
+
"version": 3
|
| 179 |
+
},
|
| 180 |
+
"file_extension": ".py",
|
| 181 |
+
"mimetype": "text/x-python",
|
| 182 |
+
"name": "python",
|
| 183 |
+
"nbconvert_exporter": "python",
|
| 184 |
+
"pygments_lexer": "ipython3",
|
| 185 |
+
"version": "3.10.17"
|
| 186 |
+
}
|
| 187 |
+
},
|
| 188 |
+
"nbformat": 4,
|
| 189 |
+
"nbformat_minor": 2
|
| 190 |
+
}
|
test_asr.py
ADDED
|
@@ -0,0 +1,23 @@
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from server import gt
|
| 2 |
+
import librosa
|
| 3 |
+
ref_audio, _ = librosa.load('/home/salman/salman/minomni_sn21/omega-v2v/miner_models/MiniCPM-o/assets/input_examples/assistant_female_voice.wav', sr=16000, mono=True) # load the reference audio
|
| 4 |
+
|
| 5 |
+
text = gt(ref_audio, 16_000)
|
| 6 |
+
print(text)
|
| 7 |
+
|
| 8 |
+
# write a code to recursively iterate a directory and subdirectories to transcript all audio .wav files in it
|
| 9 |
+
import os
|
| 10 |
+
def transcribe_directory():
|
| 11 |
+
for root, dirs, files in os.walk('/home/salman/salman/minomni_sn21/omega-v2v/miner_models/recordings'):
|
| 12 |
+
for file in files:
|
| 13 |
+
if file.endswith('.wav'):
|
| 14 |
+
print(f"Processing file: {file}")
|
| 15 |
+
file_path = os.path.join(root, file)
|
| 16 |
+
audio, sr = librosa.load(file_path, sr=16000, mono=True)
|
| 17 |
+
transcription = gt(audio, sr)
|
| 18 |
+
print(f"Transcription for {file_path}: {transcription}")
|
| 19 |
+
with open(file_path.replace('.wav', '.txt'), 'w') as f:
|
| 20 |
+
f.write(transcription)
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
transcribe_directory()
|