初始化项目，由ModelHub XC社区提供模型

Model: Cnam-LMSSC/wav2vec2-spanish-phonemizer
Source: Original Platform

.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,149 @@
+---
+library_name: transformers
+license: mit
+language: es
+metrics:
+- per
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- phonemize
+- phoneme
+datasets:
+- facebook/multilingual_librispeech
+model-index:
+- name: Wav2Vec2-base Spanish finetuned for phonemes by LMSSC
+  results:
+  - task:
+      type: automatic-speech-recognition
+      name: Speech Recognition
+    dataset:
+      name: Multilingual Librispeech
+      type: facebook/multilingual_librispeech
+      args: es
+    metrics:
+    - type: per
+      value: 2.94
+      name: Test PER on Multilingual Librispeech ES | Trained
+    - type: per
+      value: 2.66
+      name: Val PER on Multilingual Librispeech ES | Trained
+---
+
+# Fine-tuned Spanish Voxpopuli v2 wav2vec2-base model for speech-to-phoneme task in Spanish
+
+Fine-tuned [facebook/wav2vec2-base-es-voxpopuli-v2](https://huggingface.co/facebook/wav2vec2-base-es-voxpopuli-v2) for **Spanish speech-to-phoneme** (without language model) using the train and validation splits of [Multilingual Librispeech](https://huggingface.co/datasets/facebook/multilingual_librispeech).
+
+## Audio samplerate for usage 
+
+When using this model, make sure that your speech input is **sampled at 16kHz**.
+
+## Output
+
+As this model is specifically trained for a speech-to-phoneme task, the output is sequence of [IPA-encoded](https://en.wikipedia.org/wiki/International_Phonetic_Alphabet) words, without punctuation.
+If you don't read the phonetic alphabet fluently, you can use this excellent [IPA reader website](http://ipa-reader.xyz) to convert the transcript back to audio synthetic speech in order to check the quality of the phonetic transcription.
+
+## Training procedure
+
+The model has been finetuned on Multilingual Librispeech (ES) for 30 epochs on a 1xADA_6000 GPU at Cnam/LMSSC using a ddp strategy and gradient-accumulation procedure (256 audios per update, corresponding roughly to 25 minutes of speech per update -> 2k updates per epoch)
+
+- Learning rate schedule : Double Tri-state schedule
+    - Warmup from 1e-5 for 7% of total updates
+    - Constant at 1e-4 for 28% of total updates
+    - Linear decrease to 1e-6 for 36% of total updates
+    - Second warmup boost to 3e-5 for 3% of total updates
+    - Constant at 3e-5 for 12% of total updates
+    - Linear decrease to 1e-7 for remaining 14% of updates
+ 
+- The set of hyperparameters used for training are the same as those detailed in Annex B and Table 6 of [wav2vec2 paper](https://arxiv.org/pdf/2006.11477.pdf).
+
+## Usage (using the online Inference API)
+
+Just record your voice on the ⚡ Inference API on this webpage, and then click on "Compute", that's all ! 
+
+## Usage (with HuggingSound library)
+
+The model can be used directly using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library:
+
+```python
+import pandas as pd
+from huggingsound import SpeechRecognitionModel
+
+model = SpeechRecognitionModel("Cnam-LMSSC/wav2vec2-spanish-phonemizer")
+audio_paths = ["./test_rilettura_testo.wav", "./10179_11051_000021.flac"]
+
+# No need for the Audio files to be sampled at 16 kHz here,
+# they are automatically resampled by Huggingsound
+
+transcriptions = model.transcribe(audio_paths)
+
+# (Optionnal) Display results in a table :
+## transcriptions is list of dicts also containing timestamps and probabilities !
+
+df = pd.DataFrame(transcriptions)
+df['Audio file'] = pd.DataFrame(audio_paths)
+df.set_index('Audio file', inplace=True)
+df[['transcription']]
+```
+
+**Output** : 
+
+| **Audio file**                 | **Phonetic transcription (IPA)**                                                                                                                                           |
+|:---------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------|
+| ./prueba_revision_texto.wav | paɾeθia un tiβuɾon kompleto ðe βeɾas ke si asi si aoɾa koxemos a aθɛntwaða este βlak ðoɡ ʝa tendɾemos notiθjas ke embjaɾ a aθɛntwaða nwestɾo βwem patɾon el kaβaʎeɾo    |
+| ./10179_11051_000021.flac  | pestaɲeaðo keðose en donde estaβa apoʝandose apenas en su muleta i kon los oxos klaβaðos en su kompaɲeɾo komo una βiβoɾa lista paɾa aβalanθaɾse          |
+
+## Inference script (if you do not want to use the huggingsound library) : 
+
+```python
+import torch
+from transformers import AutoModelForCTC, Wav2Vec2Processor
+from datasets import load_dataset
+import soundfile as sf # Or Librosa if you prefer to ... 
+
+MODEL_ID = "Cnam-LMSSC/wav2vec2-spanish-phonemizer"
+
+model = AutoModelForCTC.from_pretrained(MODEL_ID)
+processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
+
+audio = sf.read('example.wav')
+# Make sure you have a 16 kHz sampled audio file, or resample it !
+
+inputs = processor(np.array(audio[0]),sampling_rate=16_000., return_tensors="pt")
+
+with torch.no_grad():
+  logits = model(**inputs).logits
+
+predicted_ids = torch.argmax(logits,dim = -1)
+transcription = processor.batch_decode(predicted_ids)
+
+print("Phonetic transcription : ", transcription)
+```
+
+**Output** : 
+
+'esˈtoj ˈmuj konˈtento ðe pɾesenˈtaɾles ˈnwestɾa soluˈsjon ˈpaɾa fonemiˈsaɾ ˈawðjos ˈfasilˈmente | funˈsjona βasˈtante ˈβjen'
+
+## Test Results:
+
+In the table below, we report the Phoneme Error Rate (PER) of the model on Multilingual Librispeech (using the Spanish configs for the dataset of course) : 
+
+| Model | Test Set  | PER |
+| ------------- | ------------- | ------------- |
+| Cnam-LMSSC/wav2vec2-spanish-phonemizer | Multilingual Librispeech (Spanish) | **2.94%** |
+
+
+## Citation
+If you use this finetuned model for any publication, please use this to cite our work :
+
+```bibtex
+@misc {lmssc-wav2vec2-base-phonemizer-spanish_2026,
+	author       = { Olivier, Malo },
+	title        = { wav2vec2-spanish-phonemizer (Revision 4c60fe7) },
+	year         = 2026,
+	url          = { https://huggingface.co/Cnam-LMSSC/wav2vec2-spanish-phonemizer },
+	doi          = { 10.57967/hf/8136 },
+	publisher    = { Hugging Face }
+}
+```

added_tokens.json

@@ -0,0 +1,4 @@
+{
+  "</s>": 39,
+  "<s>": 38
+}

config.json

@@ -0,0 +1,117 @@
+{
+  "activation_dropout": 0.0,
+  "adapter_attn_dim": null,
+  "adapter_kernel_size": 3,
+  "adapter_stride": 2,
+  "add_adapter": false,
+  "apply_spec_augment": true,
+  "architectures": [
+    "Wav2Vec2ForCTC"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 1,
+  "classifier_proj_size": 256,
+  "codevector_dim": 256,
+  "contrastive_logits_temperature": 0.1,
+  "conv_bias": false,
+  "conv_dim": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
+    10,
+    3,
+    3,
+    3,
+    3,
+    2,
+    2
+  ],
+  "conv_stride": [
+    5,
+    2,
+    2,
+    2,
+    2,
+    2,
+    2
+  ],
+  "ctc_loss_reduction": "sum",
+  "ctc_zero_infinity": false,
+  "diversity_loss_weight": 0.1,
+  "do_stable_layer_norm": false,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
+  "feat_extract_norm": "group",
+  "feat_proj_dropout": 0.1,
+  "feat_quantizer_dropout": 0.0,
+  "final_dropout": 0.0,
+  "freeze_feat_extract_train": true,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.0,
+  "mask_channel_length": 10,
+  "mask_channel_min_space": 1,
+  "mask_channel_other": 0.0,
+  "mask_channel_prob": 0.0,
+  "mask_channel_selection": "static",
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_min_space": 1,
+  "mask_time_other": 0.0,
+  "mask_time_prob": 0.05,
+  "mask_time_selection": "static",
+  "model_type": "wav2vec2",
+  "no_mask_channel_overlap": false,
+  "no_mask_time_overlap": false,
+  "num_adapter_layers": 3,
+  "num_attention_heads": 12,
+  "num_codevector_groups": 2,
+  "num_codevectors_per_group": 320,
+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 12,
+  "num_negatives": 100,
+  "output_hidden_size": 768,
+  "pad_token_id": 37,
+  "proj_codevector_dim": 256,
+  "tdnn_dilation": [
+    1,
+    2,
+    3,
+    1,
+    1
+  ],
+  "tdnn_dim": [
+    512,
+    512,
+    512,
+    512,
+    1500
+  ],
+  "tdnn_kernel": [
+    5,
+    3,
+    3,
+    1,
+    1
+  ],
+  "transformers_version": "4.57.3",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 40,
+  "xvector_output_dim": 512
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7908db3be13eb7c0012a9479dea3746d25152c6ea24d041e07b1a28f6e20fa2f
+size 377635736

preprocessor_config.json

@@ -0,0 +1,10 @@
+{
+  "do_normalize": true,
+  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "processor_class": "Wav2Vec2Processor",
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:40e48162bf60d7443258212f4c2e7fec4363f1f879b8664a369a57e156e30701
+size 377681271

special_tokens_map.json

@@ -0,0 +1,6 @@
+{
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "pad_token": "[PAD]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,49 @@
+{
+  "added_tokens_decoder": {
+    "36": {
+      "content": "[UNK]",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": false
+    },
+    "37": {
+      "content": "[PAD]",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": false
+    },
+    "38": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "39": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "do_lower_case": false,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "processor_class": "Wav2Vec2Processor",
+  "replace_word_delimiter_char": " ",
+  "target_lang": null,
+  "tokenizer_class": "Wav2Vec2CTCTokenizer",
+  "unk_token": "[UNK]",
+  "word_delimiter_token": "|"
+}

vocab.json

@@ -0,0 +1,40 @@
+{
+  "[PAD]": 37,
+  "[UNK]": 36,
+  "a": 1,
+  "b": 2,
+  "d": 3,
+  "e": 4,
+  "f": 5,
+  "i": 6,
+  "j": 7,
+  "k": 8,
+  "l": 9,
+  "m": 10,
+  "n": 11,
+  "o": 12,
+  "p": 13,
+  "r": 14,
+  "s": 15,
+  "t": 16,
+  "u": 17,
+  "w": 18,
+  "x": 19,
+  "|": 0,
+  "ð": 20,
+  "ŋ": 21,
+  "ɛ": 22,
+  "ɡ": 23,
+  "ɣ": 24,
+  "ɪ": 25,
+  "ɲ": 26,
+  "ɾ": 27,
+  "ʃ": 28,
+  "ʊ": 29,
+  "ʎ": 30,
+  "ʒ": 31,
+  "ʝ": 32,
+  "ː": 33,
+  "β": 34,
+  "θ": 35
+}