Merge branch 'main' of https://huggingface.co/microsoft/unispeech-1350-en-90-it-ft-1h into main

README.md

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+---
+language:
+- it
+datasets:
+- common_voice
+tags:
+- audio
+- automatic-speech-recognition
+---
+
+# UniSpeech-Large-plus ITALIAN
+
+[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
+
+The large model pretrained on 16kHz sampled speech audio and phonetic labels and consequently fine-tuned on 1h of Italian phonemes. 
+When using the model make sure that your speech input is also sampled at 16kHz and your text in converted into a sequence of phonemes. 
+
+[Paper: UniSpeech: Unified Speech Representation Learning
+with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597)
+
+Authors: Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang
+
+**Abstract**
+*In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data, in which supervised phonetic CTC learning and phonetically-aware contrastive self-supervised learning are conducted in a multi-task learning manner. The resultant representations can capture information more correlated with phonetic structures and improve the generalization across languages and domains. We evaluate the effectiveness of UniSpeech for cross-lingual representation learning on public CommonVoice corpus. The results show that UniSpeech outperforms self-supervised pretraining and supervised transfer learning for speech recognition by a maximum of 13.4% and 17.8% relative phone error rate reductions respectively (averaged over all testing languages). The transferability of UniSpeech is also demonstrated on a domain-shift speech recognition task, i.e., a relative word error rate reduction of 6% against the previous approach.*
+
+The original model can be found under https://github.com/microsoft/UniSpeech/tree/main/UniSpeech.
+
+# Usage
+
+This is an speech model that has been fine-tuned on phoneme classification.
+
+## Inference
+
+```python
+import torch
+from datasets import load_dataset
+from transformers import AutoModelForCTC, AutoProcessor
+import torchaudio.functional as F
+
+model_id = "microsoft/unispeech-1350-en-90-it-ft-1h"
+
+sample = next(iter(load_dataset("common_voice", "it", split="test", streaming=True)))
+resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
+
+model = AutoModelForCTC.from_pretrained(model_id)
+processor = AutoProcessor.from_pretrained(model_id)
+
+input_values = processor(resampled_audio, return_tensors="pt").input_values
+
+with torch.no_grad():
+    logits = model(input_values).logits
+
+prediction_ids = torch.argmax(logits, dim=-1)
+transcription = processor.batch_decode(prediction_ids)
+# => 'bien y qué regalo vas a abrir primero'
+```
+
+## Evaluation
+
+```python
+from datasets import load_dataset, load_metric                                                 
+import datasets                                                                                
+import torch
+from transformers import AutoModelForCTC, AutoProcessor
+
+model_id = "microsoft/unispeech-1350-en-90-it-ft-1h"
+                                                                                                                                                                                              
+ds = load_dataset("mozilla-foundation/common_voice_3_0", "it", split="train+validation+test+other")
+wer = load_metric("wer") 
+
+model = AutoModelForCTC.from_pretrained(model_id)
+processor = AutoProcessor.from_pretrained(model_id)
+                                               
+# taken from
+# https://github.com/microsoft/UniSpeech/blob/main/UniSpeech/examples/unispeech/data/it/phonesMatches_reduced.json
+
+with open("./testSeqs_uniform_new_version.text", "r") as f:
+    lines = f.readlines()
+
+                                                                                               
+# retrieve ids model is evaluated on
+ids = [x.split("\t")[0] for x in lines]
+                                               
+                                                                                                                                                                                              
+ds = ds.filter(lambda p: p.split("/")[-1].split(".")[0] in ids, input_columns=["path"])                 
+
+ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000))
+                                                                                               
+
+def decode(batch):
+    input_values = processor(batch["audio"]["array"], return_tensors="pt", sampling_rate=16_000)
+    logits = model(input_values).logits
+                                                                                               
+    pred_ids = torch.argmax(logits, axis=-1)                                                   
+
+    batch["prediction"] = processor.batch_decode(pred_ids)
+    batch["target"] = processor.tokenizer.phonemize(batch["sentence"])   
+
+    return batch  
+                                                                                                                                                                                              
+                                               
+out = ds.map(decode, remove_columns=ds.column_names)                                           
+per = wer.compute(predictions=out["prediction"], references=out["target"])                 
+
+print("per", per)  
+# -> should give per 0.06685252146070828
+```
+
+# Contribution
+
+The model was contributed by [cywang](https://huggingface.co/cywang) and [patrickvonplaten](https://huggingface.co/patrickvonplaten).
+
+# License
+
+The official license can be found [here](https://github.com/microsoft/UniSpeech/blob/main/LICENSE)
+
+See *UniSpeeech-L^{+}* - *it*:
+
+![design](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/unispeech_results.png)